Saud M Al-Fattah
Dr. Saud M. Al-Fattah (Al-Thuwaini) is a Corporate Consultant of Economic and Energy Analysis at Corporate Planning of Saudi Aramco. Prior to this position, he was Director and Fellow of Global Energy Markets and Economics Research at the King Abdullah Petroleum Studies and Research Center (KAPSARC), seconded from Saudi Aramco from 2008 to 2013. Saud was a pioneering member of the development team of KAPSARC where he made significant contributions in the development of research agenda and programs, research strategy, research projects, recruitment, and collaboration models and activities.
Saud has more than 25 years of experience with Saudi Aramco working in several departments including corporate planning, reservoir management, oil and gas reserves assessment and fields’ development studies, reservoir simulation and reservoir engineering systems. His areas of specialty include: reservoir management, energy markets and economics, artificial intelligence, operations research and management, and strategy management.
Saud has been granted a U.S. patent, published several technical papers in peer-reviewed journals and conferences proceedings, made several presentations in different conferences and symposia, and authored and co-authored three books: “Artificial Intelligence and Data Mining Applications in the E&P Industry,” “Carbon Capture and Storage: Technologies, Policies, Economics, and Implementation Strategies,” and “Innovative Methods for Analyzing and Forecasting World Gas Supply”. Saud is a member of the Society of Petroleum Engineers (SPE), the International Association for Energy Economics (IAEE), the Arab Energy Club, the European Association of Geoscientists & Engineers (EAGE), and Tomouh. He is also a member of the SPE Artificial Intelligence & Petroleum Analytics Subcommittee.
Saud is a technical editor for the SPE Reservoir Evaluation & Engineering Journal, Energy Policy Journal, and Journal of Natural Gas Science and Engineering. He held the positions of chairman of the 2007 SPE Saudi Arabia Annual Technical Symposium, and vice chairman of the 2006 SPE Saudi Arabia Annual Technical Symposium. He was also a member of the organizing and technical committees of 2011 KAPSARC Energy Dialogue, Riyadh, Saudi Arabia.
Saud earned his Ph.D. (with distinction) from Texas A&M University, and M.S. and B.S. degrees (with honors) from King Fahd University of Petroleum and Minerals (KFUPM), all in petroleum engineering. He also earned his Executive MBA degree (with honors; first-in-class) from Prince Mohammad Bin Fahd University (PMU), Saudi Arabia.
Address: Saudi Aramco, Dhahran 31311, Saudi Arabia
Saud has more than 25 years of experience with Saudi Aramco working in several departments including corporate planning, reservoir management, oil and gas reserves assessment and fields’ development studies, reservoir simulation and reservoir engineering systems. His areas of specialty include: reservoir management, energy markets and economics, artificial intelligence, operations research and management, and strategy management.
Saud has been granted a U.S. patent, published several technical papers in peer-reviewed journals and conferences proceedings, made several presentations in different conferences and symposia, and authored and co-authored three books: “Artificial Intelligence and Data Mining Applications in the E&P Industry,” “Carbon Capture and Storage: Technologies, Policies, Economics, and Implementation Strategies,” and “Innovative Methods for Analyzing and Forecasting World Gas Supply”. Saud is a member of the Society of Petroleum Engineers (SPE), the International Association for Energy Economics (IAEE), the Arab Energy Club, the European Association of Geoscientists & Engineers (EAGE), and Tomouh. He is also a member of the SPE Artificial Intelligence & Petroleum Analytics Subcommittee.
Saud is a technical editor for the SPE Reservoir Evaluation & Engineering Journal, Energy Policy Journal, and Journal of Natural Gas Science and Engineering. He held the positions of chairman of the 2007 SPE Saudi Arabia Annual Technical Symposium, and vice chairman of the 2006 SPE Saudi Arabia Annual Technical Symposium. He was also a member of the organizing and technical committees of 2011 KAPSARC Energy Dialogue, Riyadh, Saudi Arabia.
Saud earned his Ph.D. (with distinction) from Texas A&M University, and M.S. and B.S. degrees (with honors) from King Fahd University of Petroleum and Minerals (KFUPM), all in petroleum engineering. He also earned his Executive MBA degree (with honors; first-in-class) from Prince Mohammad Bin Fahd University (PMU), Saudi Arabia.
Address: Saudi Aramco, Dhahran 31311, Saudi Arabia
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Papers by Saud M Al-Fattah
For demonstration purposes, the GIS initially uses publicly available data, but ultimately aims at integrating actual data provided by relevant entities in Saudi Arabia. For a given case, experts can identify suitable zones for CO2 storage by superimposing all of the GIS information, which is structured in layers. Also, the system can determine the theoretical volume of CO2 that can be stored based upon the calculation methods of the Carbon Storage Leadership Forum/Department of Energy (CSLF/DOE), and the optimal CO2 transport routes to connect one or several CO2 emitters to the given storage site(s), while taking into account the different constraints or opportunities. A case example is provided to highlight the key features of the GIS / DSS system.
shifted strongly towards identifying the role of financial instruments in shaping oil price movements. Although it is important to understand these short-run issues, a large vacuum exists between explanations that track short-run volatility within the context of long-run equilibrium conditions.
The theories and models of oil demand and supply that are reviewed in this paper, although imperfect in many respects, offer a clear and well-defined perspective on the forces that are shaping the markets for crude oil and refined products.
The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. There are several kinds of models have been proposed, including structural, computational and reduced form models.
Recently, artificial intelligence was also introduced.
This paper provides: (1) model taxonomy and the uses of models providing the motivation for its preparation, (2) a brief chronology explaining how oil market models have evolved over time, (3) three different model types: structural, computational, and reduced form models, and (4) artificial
intelligence and data mining for oil market models.
The daily Brent spot price fluctuated between $30 and above $140 per barrel since the beginning of 2004. Both fundamental and financial explanations have been offered as explanatory factors. This paper selectively reviews the voluminous literature on oil price determinants since the early 1970s. It concludes that most researchers attribute the long-run oil price path to fundamental factors such as economic growth, resource depletion, technical advancements in both oil supply and demand, and the market organization of major oil petroleum exporting countries (OPEC). Short-run price movements are more difficult to explain. Many researchers attribute short-run price movements to fundamental supply and demand factors in a market with very little quantity response to price changes. Nevertheless, there appears to be some evidence of occasional financial bubbles particularly in months leading up to the financial collapse in 2008. These conflicting stories will not be properly integrated without a meeting of the minds between financial and energy economists.
Previously, U.S. commitment to the region was taken for granted as the country relied heavily on oil imports from the Gulf. However, recent forecasts by organizations such as the International Energy Agency (IEA) and BP have predicted that the U.S. will become energy self-sufficient within the next couple decades. , It is now an open question whether the U.S. will continue to secure shipping lanes in the Strait of Hormuz.
We argue that the U.S. has two overarching interests at stake in securing the Strait of Hormuz: energy security and geopolitics. A supply disruption in the Strait of Hormuz is currently not an existential threat to the survival of the U.S. Thus, energy security in this case can be thought of as economic vulnerability to a disruption in oil supplies. Geopolitical interests represent broader strategic concerns such as countering Iran in the Gulf. Analyzing how energy self-sufficiency affects these core interests is key to understanding future U.S. policy.
Despite surging domestic production, the U.S. will still rely on imports to meet at least 30% of its oil consumption by 2035. More importantly, 82% of the transport sector’s energy needs will be met by oil-based fuels. The U.S. economy will thus continue to be vulnerable to a disruption in shipping lanes through the Strait of Hormuz. Moreover, the global reliance on exports from the Gulf will increase as oil transported through the Strait of Hormuz will rise from 35% of world oil exports in 2010 to 50% in 2035, according to the IEA. Any reduction to growth in the world economy caused by a supply disruption will also reduce demand for U.S. goods and services. Regardless of domestic oil production and consumption, in the foreseeable future the U.S. will never be fully insulated from a disruption in shipping through the Strait of Hormuz.
Maintaining a presence in the Strait of Hormuz will continue to be a key part of America’s geopolitical strategy. Military assets in the Gulf help the U.S. to counter Iran’s influence and deter aggression. Gulf security will also be an integral part to the U.S. “Asia pivot” as oil exports through the Strait of Hormuz represent a growing share of Asian oil consumption. Because oil will be an extremely important commodity for rising powers India and China, the U.S. cannot compromise security in Hormuz without upsetting the international order and hastening its decline as the global super power.
Therefore, energy self-sufficiency will not have a significant impact on America’s desire to secure the Strait of Hormuz; however, due to political and fiscal constraints, the U.S. may be forced to reduce its military spending regardless. Political dysfunction in Washington has caused the Federal Government to implement mandatory and arbitrary cuts to defense spending in what is known as the “Sequester”. Over the longer term, rising costs of entitlements coupled with reluctance to raise tax revenue threaten to crowd out defense spending.
Given the strategic importance of the Strait of Hormuz, it is unlikely that the U.S. will choose to withdraw. Even if the U.S. must reduce its military spending in the Gulf, a growing partnership between America and the Gulf Cooperation Council (GCC) countries may allow the U.S. to maintain security in Hormuz with a smaller military footprint. Table 1 summarizes the main factors and their effect on U.S. commitment to secure the Strait of Hormuz.
There are three potential scenarios for U.S. engagement in Hormuz outlined in this report. In the first scenario, “Maintaining the Status Quo,” the U.S. sustains a large, unilateral force in the Gulf. In the second scenario, “Strategic Partnerships,” the U.S. reduces its overall footprint but ensures security through a close partnership with GCC countries. In the third scenario, “Limited Engagement,” the U.S. withdraws from the Gulf and cedes leadership roles to a rising China. The most likely outcome will be a combination of scenarios one and two.
Based on the analysis of this report, there are two main policy implications. First, policymakers in the U.S. should emphasize the importance of shipping lanes in the Gulf and their continued relevance to the U.S. interests. Second, GCC states and U.S. policymakers should realize the importance of forging stronger defense partnerships. Not only do their interests converge, but a partnership could allow the U.S. to maintain security with a reduced budget.
dramatically in recent years and new approaches
are needed to understand, model, and forecast oil
prices today. In addition to the commencement of the
financialization era in oil markets, there have been
structural changes in the global oil market. Financial
instruments are communicating information about
future conditions much more rapidly than in the past.
Prices from long and short-duration contracts have
started moving more together. Abrupt changes in supply
and demand, influenced by such events and trends
as the financial crisis of 2008-09, uncertainty about
China’s economic growth rate, the Libyan uprising,
the Iranian Nuclear standstill, and the Deepwater
Horizon oil spill, change expectations and current
prices. Although volatility appears greater over this
period, financialization makes price discovery more
robust. Most empirical economic studies suggest that
fundamental factors shaped the expectations over 2004-
08, although financial bubbles may have emerged just
prior to and during the summer of 2008.
With increased price volatility, major exporters are
considering ways to achieve more price stability to
improve long-term production and consumption
decisions. Managing excess capacity has historically
been an important method for keeping world crude oil
prices stable during periods of sharp supply or demand
shifts. Building and maintaining excess capacity in
current markets allows greater price stability when
Asian economic growth accelerates suddenly or during
periods of supply uncertainty in major oil producing
regions. OPEC can contribute to price stability more
easily when members agree on the best use of oil
production capacity.
Important structural changes have emerged in
the global oil market after major price increases.
Partially motivated by governments' policies, major
developments in energy and oil efficiencies occurred
after the oil price increases of the early and the late
1970s, such as improvements in vehicle fuel efficiency,
building codes, power grids, and energy systems. On
the supply side, seismic imaging and horizontal drilling,
as well as favorable tax regimes, expanded production
capacity in countries outside OPEC. After the oil price
increases of 2004-08, investments in oil sands, deep
water, biofuels, and other non-conventional sources of
energy accelerated. Recent improvements in shale gas production could well be transferred to oil-producing
activities, resulting in expanded oil supplies in areas that
were previously considered prohibitively expensive. The
search for alternative transportation fuels continues
with expanded research into compressed natural gas,
biofuels, diesel made from natural gas, and electric
vehicles.
In spite of these advances, some aspects of the world
oil market are not well understood. Despite numerous
attempts to model the behavior of OPEC and its
members, there exists no credible, verifiable theory about
the behavior of this 50 year-old organization. OPEC has
not acted like a monolithic cartel, constraining supplies
to raise prices. Empirical evidence suggests that at
some times, members coordinate supply responses and
at other times they compete with each other. Supplyrestraint
strategies include slower capacity expansions,
as well as curtailed production from existing capacity.
Regional political considerations and broader economic
goals beyond oil are influential factors in a country’s oil
decisions. Furthermore, the economies and financial
needs of OPEC members have changed dramatically
since the 1970s and 1980s.
This review represents a broad survey of economic
research and literature related to the structure and
functioning of the world oil market. The theories
and models of oil demand and supply reviewed here,
although imperfect in many respects, offer a clear and
well-defined perspective on the forces that are shaping
the markets for crude oil and refined products. Much
work remains to be done if we are to achieve a more
complete understanding of these forces and the trends
that lie ahead. The contents that follow represent an
assessment of how far we have come and where we
are headed. Around the world governments, businesses
and consumers share a vital interest in the benefits that
flow from an efficient, well-functioning oil market. It is
hoped, therefore, that the discussion in this review will
find a broad audience.
The continued rise of NOCs, accelerated by high oil prices, has seen the balance of control over most of the world’s hydrocarbon resources shift decisively in their favor. Their ability to access capital, human resources and technical services directly from oil field service companies, and to build in-house competencies, allows them to operate independently of Investor Owned Companies in most instances.
The demand on NOCs continues to evolve with the global energy landscape to reflect variations in demand, discovery of new ultra-deep water oil deposits, and national and geopolitical developments. NOCs, traditionally viewed as the custodians of their country's natural resources, have generally owned and managed the complete national oil and gas supply chain from upstream to downstream activities. Having secured their home base, NOCs have emerged as joint venture partners with the IOCs and increasingly as their competitors, seeking international upstream and downstream acquisition and asset targets.
The key question is whether this emerging landscape will undermine the sustainability of the IOC resource-ownership business model. Are the challenges of declining production in existing oil fields replacing oil and gas reserves in restricted access or higher cost areas, and the declining of the operating profit margins yet sufficient to reach a tipping point?
NOCs and OFSCs have increasing power and influence in global oil markets. In parallel, IOCs’ significance and role in the oil markets has been in decline due to shrinking technical skills and expertise, reduced access to low cost reserves, and lower operating profit margins. As a result, IOCs have tended to focus on more challenging and less profitable domains, shale gas, unconventional oil, and deep-water operations. OFSCs have been offering NOCs more services and specialized operations with high technical experience at a lower cost than IOCs offer. As these trends continue, IOCs are likely to adopt a new business model that may require changes in collaborative efforts and cooperative relationships. Partnering with IOCs and OFSCs is a good step for NOCs that undertake a globalization strategy. In fact, this is a win-win strategy for all parties, as it will enable IOCs to gain more access to NOCs’ resources. Further, IOCs and OFSCs in partnership with NOCs should contribute to the socioeconomic development of the countries in which they operate.
Asian state-owned companies of NOCs, most prominently from China and India, are at the forefront of strategic cross-border investments as their governments seek to prepare for long-term energy supply challenges. At the same time, increasing oil wealth brought about by rising oil prices has encouraged governments as diverse as Russia, Venezuela, Bolivia, and Ecuador to give greater political and economic leverage to their national energy champions. This is achieved in their local market through revisions to constitutional laws, contracts, tax and royalty structures. Also, the NOCs have begun to enter the international market, engaging in strategic investment activities and acquiring full or partial control of foreign companies in sectors of strategic interest for national development.
Within the Gulf Cooperation Council (GCC) region, there are a number of national oil companies that have capabilities to expand beyond serving their domestic markets. This process is, in part, being hindered by the inadequacy of corporate structures and the lack of information in the GCC region. Globally, it is being hindered by the rise of economic nationalism and the debate around economic sovereignty, security, and ownership of assets, and the perception in the west that NOCs should not seek to acquire international oil companies and assets. Undoubtedly, political considerations influence and impact the international investment policy of NOCs.
The emerging trend driven by the rise of NOCs has shifted the balance of control over most of the world’s hydrocarbon resources. In the 1970s, the NOCs (super majors) controlled less than 10% of the world’s hydrocarbon resources, while in 2012 they control more than 90%. This shift has enabled NOCs to increase their ability to access capital, human resources and technical services directly, and to build in-house competencies. Further, NOCs have been increasing their ability to conduct outsourcing activities for many operations through the oilfield services companies (OFSCs), thus increasing their range of competence.
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Moreover, the shift of the NOCs business models poses challenges for IOCs and independents by questioning the sustainability of their resource-ownership business model. Among these challenges are the production decline in existing oil fields, the difficulty of replacing oil and gas reserves in limited or restricted access areas, the rapid depletion of conventional or easy-to-access oil reserves, increasing production costs of unconventional resources, and the decline of their operating profit margins.
A number of key trends in NOCs’ activities at the international level are emerging:
With more access to capital and the development of in-house expertise, there has been a movement from being upstream producers to fully integrated energy companies;
High oil prices, improved NOC management techniques, and access to capital markets mean that NOCs now have the financial resources to bid for, and complete, major international acquisitions;
While major global oil companies may be fearful of investing in unstable areas of the world or where international sanctions have been imposed, NOCs’ decisionmaking merely has to be compatible with national policy and is unlikely to be hindered by corporate governance requirements and stakeholder action;
NOCs are better able to mitigate overseas political risks through government-to-government relationships and negotiation strategies;
NOCs can tolerate international political risk because domestic operations are likely to be unaffected; and
Consortia exclusively led by NOCs are an emerging trend that will greatly impact the global oil and gas sector.
Despite these business and marketplace advantages, NOCs are not necessarily disciplined by the marketplace and, therefore, relative to IOCs, have a tendency to make economically-inefficient decisions. They also have the tendency to tolerate underproductive labor and staff bloating or, potentially, graft and other abuses on the part of national leadership. NOCs do, indeed, have many advantages relative to private corporations, most notably the political muscle of their parent government. Also, they usually at least have greater access to capital and the potential to take greater risks without fear of "betting the company."
Nevertheless, to truly be successful, NOCs should function with the discipline of a well-managed private firm and, wherever possible, segregate their national responsibilities to avoid the potential inefficiencies. If they have larger social objectives, these should be clarified and costed out so that fraud and abuse are avoided while social objectives are pursued in a cost-effective manner.
All this being said, there is indeed a rise in the NOCs, which are increasingly looking like international corporations with the full panoply of resources and with the special asset of carrying the imprimatur of their parent nation.
energy-producing countries. Large price swings can be detrimental to both producers and
consumers. Market volatility can cause infrastructure and capacity investments to be delayed,
employment losses, and inefficient investments. In sum, the growth potential for energy-producing
countries is adversely affected. Undoubtedly, greater stability of oil prices can reduce uncertainty in
energy markets, for the benefit of consumers and producers alike. Therefore, modeling and
forecasting crude oil price volatility is critical in many financial and investment applications.
The purpose of this paper to develop new predictive models for describing and forecasting
the global oil price volatility using artificial intelligence with artificial neural network (ANN)
modeling technology. Applying the novel approach of ANN, two models were successfully
developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These
models were successfully designed, trained, verified, and tested using historical oil market data. The
estimations and predictions from the ANN models closely match the historical data of WTI from
January 1994 to April 2012. They appear to capture very well the dynamics and the direction of the
oil price volatility.
These ANN models developed in this study can be used: as short-term as well as long-term
predictive tools for the direction of oil price volatility, to quantitatively examine the effects of
various physical and economic factors on future oil market volatility, to understand the effects of
different mechanisms for reducing market volatility, and to recommend policy options and
programs incorporating mechanisms that can potentially reduce the market volatility. With this
improved method for modeling oil price volatility, experts and market analysts will be able to
empirically test new approaches to mitigating market volatility. The outcome of this work provides a
roadmap for research to improve predictability and accuracy of energy and crude models.
The purpose of this paper to develop new predictive models for describing and forecasting the global oil price volatility using artificial intelligence with artificial neural network (ANN) modeling technology. Two ANN models were successfully developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These models were successfully designed, trained, verified, and tested using historical oil market data. The estimations and predictions from the ANN models closely match the historical data of WTI from January 1994 to April 2012. These models appear to capture very well the dynamics and the direction of the oil price volatility.
The ANN models developed in this study can be used: as short-term as well as long-term predictive tools for the direction of oil price volatility, to quantitatively examine the effects of various physical and economic factors on future oil market volatility, to understand the effects of different mechanisms for reducing market volatility, and to recommend policy options and programs incorporating mechanisms that can potentially reduce the market volatility. With this improved method for modeling oil price volatility, experts and market analysts will be able to empirically test new approaches to mitigating market volatility. The outcome of this work provides a roadmap for research to improve predictability and accuracy of energy and crude models.
The main purpose of this paper is to review and assess the current state of oil market volatility
knowledge. It highlights the properties and characteristics of the oil price volatility that models
seek to capture, and discuss the different modelling approaches to oil price volatility. Asymmetric
response to price change, persistence and mean reversion, structural breaks, and possible market
spillover of volatility are discussed. To complement the discussion, West Texas Intermediate futures
price data are used to illustrate these properties using non-parametric and conditional modelling
methods. The generalised autoregressive conditional heteroskedasticity-type models usually
applied in the oil price volatility literature are also explored. We additionally examine the exogenous
factors that may influence volatility in the oil markets.
investment applications. The main purpose of this paper is to review and assess the current state
of oil market volatility knowledge. It highlights the properties and characteristics of the oil price
volatility that models seek to capture, and discuss the different modeling approaches to oil price
volatility. Asymmetric response to price change, persistence and mean reversion, structural
breaks, and possible market spillover of volatility are discussed. To complement the discussion,
WTI futures price data is used to illustrate these properties using non-parametric and conditional
modeling methods. The GARCH-type models usually applied in the oil price volatility literature
are also explored. We additionally examine the exogenous factors that may influence volatility in
the oil markets.
Crude oil is the lifeblood of the 12 member-nations of the Organization of Petroleum Exporting Countries (OPEC), which produces 40% of the world's oil supply and holds three-quarters of the proven reserves. Reaching oil production peak can be alarming to OPEC members, where oil revenue is the main feeding stream of the gross national product (GNP).
Many researchers, in the past decades, tried to find an answer to this crucial and critical question. Unfortunately, crude oil supply forecasting is a challenging endeavor.
This paper presents our forecast for the OPEC countries supply of crude oil to years beyond 2050. We applied the "multi-cyclic Hubbert" approach that accurately models the oil-production history of OPEC countries, which are virtually most of the world's crude oil supplier.
The analysis indicates that the OPEC ultimate oil recovery is about 1.137 trillion barrels. Of this amount, about 0.9117 trillion barrels remain to be produced by the end of 2080, where the peak is expected to be in 2025 at the production rate of 90 million barrels per day. OPEC has 77% of the world's remaining reserves. Furthermore, most OPEC countries will peak between 2020 and 2030, and they will remain the world main supplier of crude oil for the next 200 years.
Artificial neural network (ANN) technology has proved successful and useful in solving complex structured and nonlinear problems. This paper presents a new modeling technology to predict accurately water-oil relative permeability using ANN. The ANN models of relative permeability were developed using experimental data from waterflood core tests samples collected from carbonate reservoirs of giant Saudi Arabian oil fields. Three groups of data sets were used for training, verification, and testing the ANN models. Analysis of results of the testing data set show excellent agreement with the experimental data of relative permeability. In addition, error analyses show that the ANN models developed in this study outperform all published correlations.
The benefits of this work include meeting the increased demand for conducting special core analysis, optimizing the number of laboratory measurements, integrating into reservoir simulation and reservoir management studies, and providing significant cost savings on extensive lab work and substantial required time.
The solutions provided include a combination of smart tools and automated workflows designed to improve reservoir management and surveillance processes. A candidate recognition system was developed to identify and flag problem wells that require immediate remediation. As new production and injection data become available, the system that is linked to the corporate database can automatically display these data for fast and rigorous validation. In addition, a formation damage indicator function is also calculated using field data and mapped to spot production problem areas and identify damaged wells. A daily surveillance tool, which compares the performance of individual wells to the average performance of a group of wells, is also provided to allow the reservoir and production engineers to easily identify under-performing wells, promptly intervene, and recommend best completion practices. Benefits include efficient well management and cost avoidance resulting from early intervention and remediation, while avoiding full-scale problem resolution.
Another dynamic surveillance tool was designed and views were developed to provide online access to the hydrocarbon phase behavior and petrophysical data for the R&D scientists and reservoir engineers. The tool allows integration of the hydrocarbon phase-behavior data and comparison of petrophysical data with historical production/injection data and production well logs, resulting in enhanced analysis, production optimization and data validation. Additional benefits of the smart tools and automated workflow processes include considerable timesavings, with pertinent data being automatically updated, validated and used in the analysis, leading to improved efficiency in field management practices.
We developed a NN model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The NN model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020.
The NN was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the NN. The remaining input parameters of the reduced NN included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer NN was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the NN is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the NN and containing data from 1990 to 1998, was used to verify and validate the NN performance for prediction. Analysis of the test results shows that the NN approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the NN input parameters. A comparison of forecasts between this study and other forecast is presented.
The NN model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on future gas production.
The models of relative permeability were developed using
experimental data from 46 displacement core tests from sandstone reservoirs of Saudi fields. Three empirical equations are presented to calculate oil relative permeability, water relative permeability, and the endpoint of the water relative permeability curve. The relative permeability models were derived as a function of rock and fluid properties using stepwise linear and nonlinear regression analyses. The new empirical equations were both evaluated using the data utilized in the development and validated using published data, which were not used in the development stage, against previously published equations. Statistical results show that the new empirical equations developed in this study are in better agreement with experimental data than previous empirical equations, for both the data used in the development and validation stages. The new empirical equations can be used to determine water/oil relative permeability curves for other fields provided the reservoir data fall within the range of this study.
We developed a neural network model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The network model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020.
The neural network was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the neural network. The remaining input parameters of the reduced neural network included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer neural network was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the neural network is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the network and containing data from 1990 to 1998, was used to verify and validate the network performance for prediction. Analysis of the test results shows that the neural network approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the neural network input parameters. A comparison of forecasts between this study and other forecast is presented.
The neural network model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on future gas production.
We developed a neural network model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The network model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020.
The neural network was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the neural network. The remaining input parameters of the reduced neural network included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer neural network was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the neural network is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the network and containing data from 1990 to 1998, was used to verify and validate the network performance for prediction. Analysis of the test results shows that the neural network approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the neural network input parameters. A comparison of forecasts between this study and other forecast is presented.
The neural network model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on future gas production.
For demonstration purposes, the GIS initially uses publicly available data, but ultimately aims at integrating actual data provided by relevant entities in Saudi Arabia. For a given case, experts can identify suitable zones for CO2 storage by superimposing all of the GIS information, which is structured in layers. Also, the system can determine the theoretical volume of CO2 that can be stored based upon the calculation methods of the Carbon Storage Leadership Forum/Department of Energy (CSLF/DOE), and the optimal CO2 transport routes to connect one or several CO2 emitters to the given storage site(s), while taking into account the different constraints or opportunities. A case example is provided to highlight the key features of the GIS / DSS system.
shifted strongly towards identifying the role of financial instruments in shaping oil price movements. Although it is important to understand these short-run issues, a large vacuum exists between explanations that track short-run volatility within the context of long-run equilibrium conditions.
The theories and models of oil demand and supply that are reviewed in this paper, although imperfect in many respects, offer a clear and well-defined perspective on the forces that are shaping the markets for crude oil and refined products.
The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. There are several kinds of models have been proposed, including structural, computational and reduced form models.
Recently, artificial intelligence was also introduced.
This paper provides: (1) model taxonomy and the uses of models providing the motivation for its preparation, (2) a brief chronology explaining how oil market models have evolved over time, (3) three different model types: structural, computational, and reduced form models, and (4) artificial
intelligence and data mining for oil market models.
The daily Brent spot price fluctuated between $30 and above $140 per barrel since the beginning of 2004. Both fundamental and financial explanations have been offered as explanatory factors. This paper selectively reviews the voluminous literature on oil price determinants since the early 1970s. It concludes that most researchers attribute the long-run oil price path to fundamental factors such as economic growth, resource depletion, technical advancements in both oil supply and demand, and the market organization of major oil petroleum exporting countries (OPEC). Short-run price movements are more difficult to explain. Many researchers attribute short-run price movements to fundamental supply and demand factors in a market with very little quantity response to price changes. Nevertheless, there appears to be some evidence of occasional financial bubbles particularly in months leading up to the financial collapse in 2008. These conflicting stories will not be properly integrated without a meeting of the minds between financial and energy economists.
Previously, U.S. commitment to the region was taken for granted as the country relied heavily on oil imports from the Gulf. However, recent forecasts by organizations such as the International Energy Agency (IEA) and BP have predicted that the U.S. will become energy self-sufficient within the next couple decades. , It is now an open question whether the U.S. will continue to secure shipping lanes in the Strait of Hormuz.
We argue that the U.S. has two overarching interests at stake in securing the Strait of Hormuz: energy security and geopolitics. A supply disruption in the Strait of Hormuz is currently not an existential threat to the survival of the U.S. Thus, energy security in this case can be thought of as economic vulnerability to a disruption in oil supplies. Geopolitical interests represent broader strategic concerns such as countering Iran in the Gulf. Analyzing how energy self-sufficiency affects these core interests is key to understanding future U.S. policy.
Despite surging domestic production, the U.S. will still rely on imports to meet at least 30% of its oil consumption by 2035. More importantly, 82% of the transport sector’s energy needs will be met by oil-based fuels. The U.S. economy will thus continue to be vulnerable to a disruption in shipping lanes through the Strait of Hormuz. Moreover, the global reliance on exports from the Gulf will increase as oil transported through the Strait of Hormuz will rise from 35% of world oil exports in 2010 to 50% in 2035, according to the IEA. Any reduction to growth in the world economy caused by a supply disruption will also reduce demand for U.S. goods and services. Regardless of domestic oil production and consumption, in the foreseeable future the U.S. will never be fully insulated from a disruption in shipping through the Strait of Hormuz.
Maintaining a presence in the Strait of Hormuz will continue to be a key part of America’s geopolitical strategy. Military assets in the Gulf help the U.S. to counter Iran’s influence and deter aggression. Gulf security will also be an integral part to the U.S. “Asia pivot” as oil exports through the Strait of Hormuz represent a growing share of Asian oil consumption. Because oil will be an extremely important commodity for rising powers India and China, the U.S. cannot compromise security in Hormuz without upsetting the international order and hastening its decline as the global super power.
Therefore, energy self-sufficiency will not have a significant impact on America’s desire to secure the Strait of Hormuz; however, due to political and fiscal constraints, the U.S. may be forced to reduce its military spending regardless. Political dysfunction in Washington has caused the Federal Government to implement mandatory and arbitrary cuts to defense spending in what is known as the “Sequester”. Over the longer term, rising costs of entitlements coupled with reluctance to raise tax revenue threaten to crowd out defense spending.
Given the strategic importance of the Strait of Hormuz, it is unlikely that the U.S. will choose to withdraw. Even if the U.S. must reduce its military spending in the Gulf, a growing partnership between America and the Gulf Cooperation Council (GCC) countries may allow the U.S. to maintain security in Hormuz with a smaller military footprint. Table 1 summarizes the main factors and their effect on U.S. commitment to secure the Strait of Hormuz.
There are three potential scenarios for U.S. engagement in Hormuz outlined in this report. In the first scenario, “Maintaining the Status Quo,” the U.S. sustains a large, unilateral force in the Gulf. In the second scenario, “Strategic Partnerships,” the U.S. reduces its overall footprint but ensures security through a close partnership with GCC countries. In the third scenario, “Limited Engagement,” the U.S. withdraws from the Gulf and cedes leadership roles to a rising China. The most likely outcome will be a combination of scenarios one and two.
Based on the analysis of this report, there are two main policy implications. First, policymakers in the U.S. should emphasize the importance of shipping lanes in the Gulf and their continued relevance to the U.S. interests. Second, GCC states and U.S. policymakers should realize the importance of forging stronger defense partnerships. Not only do their interests converge, but a partnership could allow the U.S. to maintain security with a reduced budget.
dramatically in recent years and new approaches
are needed to understand, model, and forecast oil
prices today. In addition to the commencement of the
financialization era in oil markets, there have been
structural changes in the global oil market. Financial
instruments are communicating information about
future conditions much more rapidly than in the past.
Prices from long and short-duration contracts have
started moving more together. Abrupt changes in supply
and demand, influenced by such events and trends
as the financial crisis of 2008-09, uncertainty about
China’s economic growth rate, the Libyan uprising,
the Iranian Nuclear standstill, and the Deepwater
Horizon oil spill, change expectations and current
prices. Although volatility appears greater over this
period, financialization makes price discovery more
robust. Most empirical economic studies suggest that
fundamental factors shaped the expectations over 2004-
08, although financial bubbles may have emerged just
prior to and during the summer of 2008.
With increased price volatility, major exporters are
considering ways to achieve more price stability to
improve long-term production and consumption
decisions. Managing excess capacity has historically
been an important method for keeping world crude oil
prices stable during periods of sharp supply or demand
shifts. Building and maintaining excess capacity in
current markets allows greater price stability when
Asian economic growth accelerates suddenly or during
periods of supply uncertainty in major oil producing
regions. OPEC can contribute to price stability more
easily when members agree on the best use of oil
production capacity.
Important structural changes have emerged in
the global oil market after major price increases.
Partially motivated by governments' policies, major
developments in energy and oil efficiencies occurred
after the oil price increases of the early and the late
1970s, such as improvements in vehicle fuel efficiency,
building codes, power grids, and energy systems. On
the supply side, seismic imaging and horizontal drilling,
as well as favorable tax regimes, expanded production
capacity in countries outside OPEC. After the oil price
increases of 2004-08, investments in oil sands, deep
water, biofuels, and other non-conventional sources of
energy accelerated. Recent improvements in shale gas production could well be transferred to oil-producing
activities, resulting in expanded oil supplies in areas that
were previously considered prohibitively expensive. The
search for alternative transportation fuels continues
with expanded research into compressed natural gas,
biofuels, diesel made from natural gas, and electric
vehicles.
In spite of these advances, some aspects of the world
oil market are not well understood. Despite numerous
attempts to model the behavior of OPEC and its
members, there exists no credible, verifiable theory about
the behavior of this 50 year-old organization. OPEC has
not acted like a monolithic cartel, constraining supplies
to raise prices. Empirical evidence suggests that at
some times, members coordinate supply responses and
at other times they compete with each other. Supplyrestraint
strategies include slower capacity expansions,
as well as curtailed production from existing capacity.
Regional political considerations and broader economic
goals beyond oil are influential factors in a country’s oil
decisions. Furthermore, the economies and financial
needs of OPEC members have changed dramatically
since the 1970s and 1980s.
This review represents a broad survey of economic
research and literature related to the structure and
functioning of the world oil market. The theories
and models of oil demand and supply reviewed here,
although imperfect in many respects, offer a clear and
well-defined perspective on the forces that are shaping
the markets for crude oil and refined products. Much
work remains to be done if we are to achieve a more
complete understanding of these forces and the trends
that lie ahead. The contents that follow represent an
assessment of how far we have come and where we
are headed. Around the world governments, businesses
and consumers share a vital interest in the benefits that
flow from an efficient, well-functioning oil market. It is
hoped, therefore, that the discussion in this review will
find a broad audience.
The continued rise of NOCs, accelerated by high oil prices, has seen the balance of control over most of the world’s hydrocarbon resources shift decisively in their favor. Their ability to access capital, human resources and technical services directly from oil field service companies, and to build in-house competencies, allows them to operate independently of Investor Owned Companies in most instances.
The demand on NOCs continues to evolve with the global energy landscape to reflect variations in demand, discovery of new ultra-deep water oil deposits, and national and geopolitical developments. NOCs, traditionally viewed as the custodians of their country's natural resources, have generally owned and managed the complete national oil and gas supply chain from upstream to downstream activities. Having secured their home base, NOCs have emerged as joint venture partners with the IOCs and increasingly as their competitors, seeking international upstream and downstream acquisition and asset targets.
The key question is whether this emerging landscape will undermine the sustainability of the IOC resource-ownership business model. Are the challenges of declining production in existing oil fields replacing oil and gas reserves in restricted access or higher cost areas, and the declining of the operating profit margins yet sufficient to reach a tipping point?
NOCs and OFSCs have increasing power and influence in global oil markets. In parallel, IOCs’ significance and role in the oil markets has been in decline due to shrinking technical skills and expertise, reduced access to low cost reserves, and lower operating profit margins. As a result, IOCs have tended to focus on more challenging and less profitable domains, shale gas, unconventional oil, and deep-water operations. OFSCs have been offering NOCs more services and specialized operations with high technical experience at a lower cost than IOCs offer. As these trends continue, IOCs are likely to adopt a new business model that may require changes in collaborative efforts and cooperative relationships. Partnering with IOCs and OFSCs is a good step for NOCs that undertake a globalization strategy. In fact, this is a win-win strategy for all parties, as it will enable IOCs to gain more access to NOCs’ resources. Further, IOCs and OFSCs in partnership with NOCs should contribute to the socioeconomic development of the countries in which they operate.
Asian state-owned companies of NOCs, most prominently from China and India, are at the forefront of strategic cross-border investments as their governments seek to prepare for long-term energy supply challenges. At the same time, increasing oil wealth brought about by rising oil prices has encouraged governments as diverse as Russia, Venezuela, Bolivia, and Ecuador to give greater political and economic leverage to their national energy champions. This is achieved in their local market through revisions to constitutional laws, contracts, tax and royalty structures. Also, the NOCs have begun to enter the international market, engaging in strategic investment activities and acquiring full or partial control of foreign companies in sectors of strategic interest for national development.
Within the Gulf Cooperation Council (GCC) region, there are a number of national oil companies that have capabilities to expand beyond serving their domestic markets. This process is, in part, being hindered by the inadequacy of corporate structures and the lack of information in the GCC region. Globally, it is being hindered by the rise of economic nationalism and the debate around economic sovereignty, security, and ownership of assets, and the perception in the west that NOCs should not seek to acquire international oil companies and assets. Undoubtedly, political considerations influence and impact the international investment policy of NOCs.
The emerging trend driven by the rise of NOCs has shifted the balance of control over most of the world’s hydrocarbon resources. In the 1970s, the NOCs (super majors) controlled less than 10% of the world’s hydrocarbon resources, while in 2012 they control more than 90%. This shift has enabled NOCs to increase their ability to access capital, human resources and technical services directly, and to build in-house competencies. Further, NOCs have been increasing their ability to conduct outsourcing activities for many operations through the oilfield services companies (OFSCs), thus increasing their range of competence.
2
Moreover, the shift of the NOCs business models poses challenges for IOCs and independents by questioning the sustainability of their resource-ownership business model. Among these challenges are the production decline in existing oil fields, the difficulty of replacing oil and gas reserves in limited or restricted access areas, the rapid depletion of conventional or easy-to-access oil reserves, increasing production costs of unconventional resources, and the decline of their operating profit margins.
A number of key trends in NOCs’ activities at the international level are emerging:
With more access to capital and the development of in-house expertise, there has been a movement from being upstream producers to fully integrated energy companies;
High oil prices, improved NOC management techniques, and access to capital markets mean that NOCs now have the financial resources to bid for, and complete, major international acquisitions;
While major global oil companies may be fearful of investing in unstable areas of the world or where international sanctions have been imposed, NOCs’ decisionmaking merely has to be compatible with national policy and is unlikely to be hindered by corporate governance requirements and stakeholder action;
NOCs are better able to mitigate overseas political risks through government-to-government relationships and negotiation strategies;
NOCs can tolerate international political risk because domestic operations are likely to be unaffected; and
Consortia exclusively led by NOCs are an emerging trend that will greatly impact the global oil and gas sector.
Despite these business and marketplace advantages, NOCs are not necessarily disciplined by the marketplace and, therefore, relative to IOCs, have a tendency to make economically-inefficient decisions. They also have the tendency to tolerate underproductive labor and staff bloating or, potentially, graft and other abuses on the part of national leadership. NOCs do, indeed, have many advantages relative to private corporations, most notably the political muscle of their parent government. Also, they usually at least have greater access to capital and the potential to take greater risks without fear of "betting the company."
Nevertheless, to truly be successful, NOCs should function with the discipline of a well-managed private firm and, wherever possible, segregate their national responsibilities to avoid the potential inefficiencies. If they have larger social objectives, these should be clarified and costed out so that fraud and abuse are avoided while social objectives are pursued in a cost-effective manner.
All this being said, there is indeed a rise in the NOCs, which are increasingly looking like international corporations with the full panoply of resources and with the special asset of carrying the imprimatur of their parent nation.
energy-producing countries. Large price swings can be detrimental to both producers and
consumers. Market volatility can cause infrastructure and capacity investments to be delayed,
employment losses, and inefficient investments. In sum, the growth potential for energy-producing
countries is adversely affected. Undoubtedly, greater stability of oil prices can reduce uncertainty in
energy markets, for the benefit of consumers and producers alike. Therefore, modeling and
forecasting crude oil price volatility is critical in many financial and investment applications.
The purpose of this paper to develop new predictive models for describing and forecasting
the global oil price volatility using artificial intelligence with artificial neural network (ANN)
modeling technology. Applying the novel approach of ANN, two models were successfully
developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These
models were successfully designed, trained, verified, and tested using historical oil market data. The
estimations and predictions from the ANN models closely match the historical data of WTI from
January 1994 to April 2012. They appear to capture very well the dynamics and the direction of the
oil price volatility.
These ANN models developed in this study can be used: as short-term as well as long-term
predictive tools for the direction of oil price volatility, to quantitatively examine the effects of
various physical and economic factors on future oil market volatility, to understand the effects of
different mechanisms for reducing market volatility, and to recommend policy options and
programs incorporating mechanisms that can potentially reduce the market volatility. With this
improved method for modeling oil price volatility, experts and market analysts will be able to
empirically test new approaches to mitigating market volatility. The outcome of this work provides a
roadmap for research to improve predictability and accuracy of energy and crude models.
The purpose of this paper to develop new predictive models for describing and forecasting the global oil price volatility using artificial intelligence with artificial neural network (ANN) modeling technology. Two ANN models were successfully developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These models were successfully designed, trained, verified, and tested using historical oil market data. The estimations and predictions from the ANN models closely match the historical data of WTI from January 1994 to April 2012. These models appear to capture very well the dynamics and the direction of the oil price volatility.
The ANN models developed in this study can be used: as short-term as well as long-term predictive tools for the direction of oil price volatility, to quantitatively examine the effects of various physical and economic factors on future oil market volatility, to understand the effects of different mechanisms for reducing market volatility, and to recommend policy options and programs incorporating mechanisms that can potentially reduce the market volatility. With this improved method for modeling oil price volatility, experts and market analysts will be able to empirically test new approaches to mitigating market volatility. The outcome of this work provides a roadmap for research to improve predictability and accuracy of energy and crude models.
The main purpose of this paper is to review and assess the current state of oil market volatility
knowledge. It highlights the properties and characteristics of the oil price volatility that models
seek to capture, and discuss the different modelling approaches to oil price volatility. Asymmetric
response to price change, persistence and mean reversion, structural breaks, and possible market
spillover of volatility are discussed. To complement the discussion, West Texas Intermediate futures
price data are used to illustrate these properties using non-parametric and conditional modelling
methods. The generalised autoregressive conditional heteroskedasticity-type models usually
applied in the oil price volatility literature are also explored. We additionally examine the exogenous
factors that may influence volatility in the oil markets.
investment applications. The main purpose of this paper is to review and assess the current state
of oil market volatility knowledge. It highlights the properties and characteristics of the oil price
volatility that models seek to capture, and discuss the different modeling approaches to oil price
volatility. Asymmetric response to price change, persistence and mean reversion, structural
breaks, and possible market spillover of volatility are discussed. To complement the discussion,
WTI futures price data is used to illustrate these properties using non-parametric and conditional
modeling methods. The GARCH-type models usually applied in the oil price volatility literature
are also explored. We additionally examine the exogenous factors that may influence volatility in
the oil markets.
Crude oil is the lifeblood of the 12 member-nations of the Organization of Petroleum Exporting Countries (OPEC), which produces 40% of the world's oil supply and holds three-quarters of the proven reserves. Reaching oil production peak can be alarming to OPEC members, where oil revenue is the main feeding stream of the gross national product (GNP).
Many researchers, in the past decades, tried to find an answer to this crucial and critical question. Unfortunately, crude oil supply forecasting is a challenging endeavor.
This paper presents our forecast for the OPEC countries supply of crude oil to years beyond 2050. We applied the "multi-cyclic Hubbert" approach that accurately models the oil-production history of OPEC countries, which are virtually most of the world's crude oil supplier.
The analysis indicates that the OPEC ultimate oil recovery is about 1.137 trillion barrels. Of this amount, about 0.9117 trillion barrels remain to be produced by the end of 2080, where the peak is expected to be in 2025 at the production rate of 90 million barrels per day. OPEC has 77% of the world's remaining reserves. Furthermore, most OPEC countries will peak between 2020 and 2030, and they will remain the world main supplier of crude oil for the next 200 years.
Artificial neural network (ANN) technology has proved successful and useful in solving complex structured and nonlinear problems. This paper presents a new modeling technology to predict accurately water-oil relative permeability using ANN. The ANN models of relative permeability were developed using experimental data from waterflood core tests samples collected from carbonate reservoirs of giant Saudi Arabian oil fields. Three groups of data sets were used for training, verification, and testing the ANN models. Analysis of results of the testing data set show excellent agreement with the experimental data of relative permeability. In addition, error analyses show that the ANN models developed in this study outperform all published correlations.
The benefits of this work include meeting the increased demand for conducting special core analysis, optimizing the number of laboratory measurements, integrating into reservoir simulation and reservoir management studies, and providing significant cost savings on extensive lab work and substantial required time.
The solutions provided include a combination of smart tools and automated workflows designed to improve reservoir management and surveillance processes. A candidate recognition system was developed to identify and flag problem wells that require immediate remediation. As new production and injection data become available, the system that is linked to the corporate database can automatically display these data for fast and rigorous validation. In addition, a formation damage indicator function is also calculated using field data and mapped to spot production problem areas and identify damaged wells. A daily surveillance tool, which compares the performance of individual wells to the average performance of a group of wells, is also provided to allow the reservoir and production engineers to easily identify under-performing wells, promptly intervene, and recommend best completion practices. Benefits include efficient well management and cost avoidance resulting from early intervention and remediation, while avoiding full-scale problem resolution.
Another dynamic surveillance tool was designed and views were developed to provide online access to the hydrocarbon phase behavior and petrophysical data for the R&D scientists and reservoir engineers. The tool allows integration of the hydrocarbon phase-behavior data and comparison of petrophysical data with historical production/injection data and production well logs, resulting in enhanced analysis, production optimization and data validation. Additional benefits of the smart tools and automated workflow processes include considerable timesavings, with pertinent data being automatically updated, validated and used in the analysis, leading to improved efficiency in field management practices.
We developed a NN model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The NN model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020.
The NN was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the NN. The remaining input parameters of the reduced NN included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer NN was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the NN is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the NN and containing data from 1990 to 1998, was used to verify and validate the NN performance for prediction. Analysis of the test results shows that the NN approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the NN input parameters. A comparison of forecasts between this study and other forecast is presented.
The NN model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on future gas production.
The models of relative permeability were developed using
experimental data from 46 displacement core tests from sandstone reservoirs of Saudi fields. Three empirical equations are presented to calculate oil relative permeability, water relative permeability, and the endpoint of the water relative permeability curve. The relative permeability models were derived as a function of rock and fluid properties using stepwise linear and nonlinear regression analyses. The new empirical equations were both evaluated using the data utilized in the development and validated using published data, which were not used in the development stage, against previously published equations. Statistical results show that the new empirical equations developed in this study are in better agreement with experimental data than previous empirical equations, for both the data used in the development and validation stages. The new empirical equations can be used to determine water/oil relative permeability curves for other fields provided the reservoir data fall within the range of this study.
We developed a neural network model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The network model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020.
The neural network was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the neural network. The remaining input parameters of the reduced neural network included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer neural network was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the neural network is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the network and containing data from 1990 to 1998, was used to verify and validate the network performance for prediction. Analysis of the test results shows that the neural network approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the neural network input parameters. A comparison of forecasts between this study and other forecast is presented.
The neural network model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on future gas production.
We developed a neural network model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The network model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020.
The neural network was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the neural network. The remaining input parameters of the reduced neural network included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer neural network was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the neural network is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the network and containing data from 1990 to 1998, was used to verify and validate the network performance for prediction. Analysis of the test results shows that the neural network approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the neural network input parameters. A comparison of forecasts between this study and other forecast is presented.
The neural network model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on future gas production.
deployed, emphasizing the gaps to be filled in terms of research and development, technology, regulation, economics, and public acceptance.
The book is divided into three parts. The first part helps clarify the global context in which Greenhouse Gas (GHG) emissions can be analyzed, highlights the importance of fossil-fuel-producing countries in positively driving clean fossil-fuel
usage, and discusses the applicability of this technology on a global and regional level in a timely yet responsible manner. The second part provides a technical description of the elements of the CCS chain, with an emphasis on new technologies and the potential capabilities of future facilities. The third part provides a review of the economic, regulatory, social, and environmental aspects associated with CCS
development and deployment on a global scale, and offers a pragmatic way forward.
In conclusion, we provide recommendations and guidelines for sustainable/responsible CCS scale-up as a way to address prevailing global energy, environment, and climate concerns.
Artificial Intelligence and Data Mining present a new set of solutions outside of the toolbox traditionally available to and used by engineers and scientists in the exploration and production industry. Successful usage of these tools requires a new way of looking at problems since first principle physics is not used as the key to unlock the problem. Rather, it plays the role of the gatekeeper to make sure that the provided solutions make physical sense. This is due to the fact that when employing AI&DM to solve problems, the main source of information is data. Unfortunately, this characteristic of AI&DM has created confusion for some to think of AI&DM as a type of statistical analysis and since statistics (and geo-statistics in some cases) has been around for a long time, they do not realize the novelty and the potential that is offered by AI&DM.
Today with the aid of AI&DM and without directly using the principles of physics, we can build reliable, data-driven predictive models that honor physics. While in statistics predefined patterns (parametric models) are used to build models, AI&DM (being universal function approximators) do not need to be forced to conform to any pre-defined functional definitions. AI&DM provide an inductive approach to problem solving that encourages learning by following intuitive pathways from general to specific as oppose to the common deductive approach that moves from specifics to general. In other words, instead of paying exclusive attention to the details as an Aristotelian approach to the truth, AI&DM provide the alternative of “the big picture,” or the Platonic approach to the truth.
Therefore, instead of using preconceived/pre-defined models to characterize patterns in data (such as decline curve analysis in the petroleum industry) AI&DM explores and discovers existing patterns in data (no matter how complex or invisible they may seem at first glance) to build models. AI&DM’s objective is to mimic the most powerful pattern recognition engine in the universe – the human brain.
AI&DM has been used as a problem-solving tool in two different fashions, both of which are represented in the collection of SPE papers presented in this reprint of series. The most common way to use AI&DM has been as a function approximator whereby the problem is defined as an input-output system and AI&DM has been used to build a model to predict the output based on a given set of input parameters. The less common approach, in our opinion, the more challenging and potentially game-changing way of using AI&DM is developing novel workflows based on the pattern recognition power of AI&DM. These workflows can only be conceived because of the potential of this exciting technology. In the following paragraphs two workflows that are based on the pattern recognition power of AI&DM are provided as examples to clarify this point.