This study investigates how a polymer flood design can be optimized while considering geological ... more This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models.Polymer flooding can increase oil recovery, reduce water cut, and improve sweep efficiency by diverting flow to low permeable zones. We applied two different history matching approaches (manual and gradient-based) to match data from a prolonged waterflood in the Watt field, a synthetic but realistic clastic reservoir that is based on real data and captures a wide range of geological heterogeneities and uncertainties through a range of different model scenarios and model realizations. The subsequent polymer flood is then optimized using history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimizations). The aim is to study how geological uncertainties innate in clastic reservoir affect polymer flooding, and how different history matching approaches impact the predicted reservoir performance and optimal polymer flood design. A key observation from this study is that the constrained optimization gave a much narrower forecast for incremental oil recovery during polymer flooding, thereby underestimating both risk and opportunities for polymer flooding.
Summary This study presents the application of an assisted history matching workflow to a large m... more Summary This study presents the application of an assisted history matching workflow to a large mature field, which utilises an ensemble of distinct geological scenarios to improve the total misfit by up to 22%. Inadequacies in history matching of this field, which is interpreted as a simple prograding delta deposit, have led to the expansion of modelling efforts at a mature stage of field production. An ensemble of models, which represent distinctly different geological scenarios is used to account for a larger range of geological uncertainty within the history matching workflow. The effective selection and modelling of these concepts has significantly improved on the total field misfit and provided a more robust platform for optimisation analysis for ongoing development.
7th EAGE Saint Petersburg International Conference and Exhibition, 2016
This work is aimed at uncertainty quantification of flow performance in fractured reservoirs. The... more This work is aimed at uncertainty quantification of flow performance in fractured reservoirs. The study demonstrates how outcrop data on fracture distribution are integrated into uncertainty quantification workflow. It includes eliciting statistics from the outcrop data to generate ensemble of discreet fracture networks (DFNs), DFNs upscaling into permeability grids, clustering of models according to permeability patterns, choice of training images for the cluster scenarios and generation of multiple realisations, flow simulation to come up with the spread of uncertainty in the recovery performance. The influence of fracture intensity and orientation on recovery efficiency was quantified. Uncertainty was represented by multiple sets of realisations based on training images. The workflow created is applicable to any fractured reservoir studies. The data for the case study is from five outcrops in Jandaira formation from NE Brazil, Potiguar Basin was mapped by Delft University. Results of simulation are of significant importance for southern Brazil hydrocarbon fields which have reserves in similar carbonate formations. This research was focused on geometric properties of fracture networks; further investigation of physical properties of fractures and integration with well and seismic data is essential for accurate properties prediction of fields from southern Brazil.
Summary In this work we introduce a novel reservoir modelling workflow where modelling is assiste... more Summary In this work we introduce a novel reservoir modelling workflow where modelling is assisted by an entropy-driven particle swarm optimizer. Producing a representative range of reservoir models that cover geological uncertainties in an effective way is a challenging task. We therefore make use of entropy to ensure that the ensemble of generated models adequately reflects the available information and provides diversity that reflects the associated variability in fluid flow behavior. The workflow is tested on a synthetic case study of a fractured reservoir. The results indicate that the entropy-driven PSO is able to prevent the diversity of the ensemble of models from collapsing whilst staying within the bounds of a predefined expected dynamic flow response. It is also shown that the entropy-driven PSO outperforms a standard PSO in this task. Secondary outcomes from the workflow, such as a spatial entropy map, provide a great tool for further uncertainty assessment and can be used to identify swept or unswept reservoir regions and the regions where more information is needed to reduce the uncertainty.
Bayesian inference and ultrasonic velocity have been used to estimate the self-association concen... more Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chains can cause an earlier onset of asphaltene self-association. Asphaltenes constitute the heaviest and most complicated fraction of crude petroleum and include a surface-active sub-fraction. When present above a critical concentration in pure solvent, asphaltene “monomers” self-associate and form nanoaggregates. Asphaltene nanoaggregates are thought to play a significant role during the remediation of petroleum spills and seeps. When mixed with water, petroleum becomes expensive to remove from the water column by conventional methods. The main reason of this difficulty is the presence of highly surface-active asphaltenes in petroleum. The nanoaggregates are thought to surround the water droplets, m...
Summary We present a data driven workflow to improve local history match quality by identifying m... more Summary We present a data driven workflow to improve local history match quality by identifying model regions from correlation between production response and geological modelling parameters for use in an assisted history matching framework. This paper outlines the implementation and results from a large mature field case study. Regions are identified by calculating the partial correlation between individual well production misfits and uncertain geological modelling parameters across 500 models. Wells are then categorised into three groups based on their correlations: positive, negative and insignificant. A probabilistic neural network (PNN) is trained on the location of each well and its group. A map of regions can then be calculated using the PNN. The parameters used to define the region map are then varied separately in each region in an assisted history matching loop. In the full field case study, an 8.8% improvement in oil rate misfit within the positively correlated well group was achieved by regional modification of the net-to-gross multiplier, with no detrimental effect on the other groups match quality. This case study demonstrates the effective identification and utilisation of geologically and dynamically inferred regions which improve the local history match
Abu Dhabi International Petroleum Exhibition and Conference, 2015
Reservoir modeling involves the characterization of the internal gridded petrophysical properties... more Reservoir modeling involves the characterization of the internal gridded petrophysical properties distribution and the simulation of fluid production. However, a common problem associated with reservoir modeling is the highly non-linear relationship between the distribution of the petrophysical parameters (frequently with a non-stationary character) and the fluid production. To tackle this problem, this paper presents a new methodology for integrated reservoir modeling by addressing a multiscale optimization approach applied for non-stationary geostatistical history matching of complex connectivity hydrocarbon reservoirs. The methodology comprises a two staged procedure for multiscale optimization, which includes a global optimization stage followed by a local refining stage. The former consists of optimizing the geological spatial anisotropy trend by coupling the stochastic optimization over the anisotropy multiparameter space with an image generation algorithm - direct sequential ...
This paper shows the challenges related to handling multiple training images for reservoir predic... more This paper shows the challenges related to handling multiple training images for reservoir prediction. We have identified two of the main challenges in handling multiple geological scenarios by creating a lower dimensional representation of the ensemble of model realizations: (i) how to relate geological knowledge to the metric space; and (ii) how to navigate in the metric space to facilitate in model update. In this work we demonstrate how to solve the classification problem in the metric space accounting for geological knowledge from a variety of prior geological concepts. In this paper we established geological relations in the metric space by making the links to the space of geologically interpretable parameters. These results would allow us to enhance geological realism of the new models obtained through the update process in the metric space.
Traditional geostatistical seismic inversion approaches are able to account for the uncertainty r... more Traditional geostatistical seismic inversion approaches are able to account for the uncertainty related with the stochastic simulation algorithms that are used as part of the inverse methodology for the model perturbation. However, they assume stationarity and no uncertainty related with large scale geological parameters represented for example by the spatial continuity pattern and the prior probability distribution of the property to invert as estimated from well-log data. We propose a multi-scale uncertainty assessment for traditional iterative geostatistical seismic methodologies by integrating stochastic adaptive sampling and Bayesian inference to tune the variogram ranges and the prior probability distribution of the property to invert within the inverse workflow. The application of the proposed methodology to a challenging synthetic dataset showed a good convergence of the inverted seismic towards the recorded one while the local and global uncertainty were jointly assessed.
Proceedings of the 32nd International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM) 2005), Tucson, USA, 30 March - 1 April 2005, 2005
Proceedings of SPE Annual Technical Conference and Exhibition, 2010
Reservoir modelling is frequently used in the oil industry to measure the risk associated with al... more Reservoir modelling is frequently used in the oil industry to measure the risk associated with alternative production scenarios. However, reservoir models themselves contain a high level of uncertainty because of the typically very limited, sparse and multi-scaled reservoir knowledge. The effect of this uncertainty can be assessed by producing a set of diverse models that match the production data reasonably well and using these models to quantify uncertainty in predicting the future performance of the reservoir. Evolutionary and swarm intelligence algorithms have become very popular for history matching due to their simplicity and parallel implementation capacity. This paper focuses on the application of Particle Swarm Optimisation (PSO) for history matching the Brugge field (a recent SPE benchmark case study). The parameterisation of the model is based on principal component analysis (PCA) for modelling spatially correlated random fields (e.g. porosity, net-to-gross and permeabili...
SPE Oil and Gas India Conference and Exhibition, 2010
History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncer... more History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncertainty in oil industry. Currently some innovative global optimisation approaches such as evolutionary algorithms and swarm intelligence methods have gained popularity for tackling history matching problems.Particle swarm optimisation (PSO) is a swarm intelligence approach for solving optimisation problems. In this approach particles are moving points in parameter space. The position of a particle is a candidate solution to the optimisation problem. Each particle searches for better positions in parameter space by updating its velocity according to rules originally inspired by behavioural models of the movement of flocks of birds.Recently the PSO algorithm has shown to be a promising tool for finding acceptable multiple history matched models quickly (Mohamed et al., 2009). However, the algorithm control parameters which balance between exploitation–exploration trade–off are to be studied...
ABSTRACT The focus of this paper is to demonstrate an integrated characterization approach for a ... more ABSTRACT The focus of this paper is to demonstrate an integrated characterization approach for a complex deltaic reservoir by using petrophysical properties and dynamic production data. Accurately characterizing deltaic reservoirs requires a non-stationary approach to the reservoir description. By linking the non-stationary characterization step with the dynamic flow modeling, the probability of success in locating infill wells increases. In this work, is presented a hybrid method which integrates the optimization firstly in the space of the anisotropy model parameters and secondly refines it in the space of the static models with a regional perturbation technique. It is an iterative methodology for optimized history matching, using adaptive stochastic sampling in the multiparameter space, and direct sequential simulation as the engine for the image transformation of the porosity and permeability models of the reservoir. The results show coherence when compared with the true case model. The approach provides the simultaneous simulation of the morphology and the property value.
ABSTRACT Geostatistical models reproduce spatial correlation based on the available on site data ... more ABSTRACT Geostatistical models reproduce spatial correlation based on the available on site data and more general concepts about the modelled patters, e.g. training images. One of the problem of modelling natural systems with geostatistics is in maintaining realism spatial features and so they agree with the physical processes in nature. Tuning the model parameters to the data may lead to geostatistical realisations with unrealistic spatial patterns, which would still honour the data. Such model would result in poor predictions, even though although fit the available data well. Conditioning the model to a wider range of relevant data provide a remedy that avoid producing unrealistic features in spatial models. For instance, there are vast amounts of information about the geometries of river channels that can be used in describing fluvial environment. Relations between the geometrical channel characteristics (width, depth, wave length, amplitude, etc.) are complex and non-parametric and are exhibit a great deal of uncertainty, which is important to propagate rigorously into the predictive model. These relations can be described within a Bayesian approach as multi-dimensional prior probability distributions. We propose a way to constrain multi-point statistics models with intelligent priors obtained from analysing a vast collection of contemporary river patterns based on previously published works. We applied machine learning techniques, namely neural networks and support vector machines, to extract multivariate non-parametric relations between geometrical characteristics of fluvial channels from the available data. An example demonstrates how ensuring geological realism helps to deliver more reliable prediction of a subsurface oil reservoir in a fluvial depositional environment.
This study investigates how a polymer flood design can be optimized while considering geological ... more This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models.Polymer flooding can increase oil recovery, reduce water cut, and improve sweep efficiency by diverting flow to low permeable zones. We applied two different history matching approaches (manual and gradient-based) to match data from a prolonged waterflood in the Watt field, a synthetic but realistic clastic reservoir that is based on real data and captures a wide range of geological heterogeneities and uncertainties through a range of different model scenarios and model realizations. The subsequent polymer flood is then optimized using history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimizations). The aim is to study how geological uncertainties innate in clastic reservoir affect polymer flooding, and how different history matching approaches impact the predicted reservoir performance and optimal polymer flood design. A key observation from this study is that the constrained optimization gave a much narrower forecast for incremental oil recovery during polymer flooding, thereby underestimating both risk and opportunities for polymer flooding.
Summary This study presents the application of an assisted history matching workflow to a large m... more Summary This study presents the application of an assisted history matching workflow to a large mature field, which utilises an ensemble of distinct geological scenarios to improve the total misfit by up to 22%. Inadequacies in history matching of this field, which is interpreted as a simple prograding delta deposit, have led to the expansion of modelling efforts at a mature stage of field production. An ensemble of models, which represent distinctly different geological scenarios is used to account for a larger range of geological uncertainty within the history matching workflow. The effective selection and modelling of these concepts has significantly improved on the total field misfit and provided a more robust platform for optimisation analysis for ongoing development.
7th EAGE Saint Petersburg International Conference and Exhibition, 2016
This work is aimed at uncertainty quantification of flow performance in fractured reservoirs. The... more This work is aimed at uncertainty quantification of flow performance in fractured reservoirs. The study demonstrates how outcrop data on fracture distribution are integrated into uncertainty quantification workflow. It includes eliciting statistics from the outcrop data to generate ensemble of discreet fracture networks (DFNs), DFNs upscaling into permeability grids, clustering of models according to permeability patterns, choice of training images for the cluster scenarios and generation of multiple realisations, flow simulation to come up with the spread of uncertainty in the recovery performance. The influence of fracture intensity and orientation on recovery efficiency was quantified. Uncertainty was represented by multiple sets of realisations based on training images. The workflow created is applicable to any fractured reservoir studies. The data for the case study is from five outcrops in Jandaira formation from NE Brazil, Potiguar Basin was mapped by Delft University. Results of simulation are of significant importance for southern Brazil hydrocarbon fields which have reserves in similar carbonate formations. This research was focused on geometric properties of fracture networks; further investigation of physical properties of fractures and integration with well and seismic data is essential for accurate properties prediction of fields from southern Brazil.
Summary In this work we introduce a novel reservoir modelling workflow where modelling is assiste... more Summary In this work we introduce a novel reservoir modelling workflow where modelling is assisted by an entropy-driven particle swarm optimizer. Producing a representative range of reservoir models that cover geological uncertainties in an effective way is a challenging task. We therefore make use of entropy to ensure that the ensemble of generated models adequately reflects the available information and provides diversity that reflects the associated variability in fluid flow behavior. The workflow is tested on a synthetic case study of a fractured reservoir. The results indicate that the entropy-driven PSO is able to prevent the diversity of the ensemble of models from collapsing whilst staying within the bounds of a predefined expected dynamic flow response. It is also shown that the entropy-driven PSO outperforms a standard PSO in this task. Secondary outcomes from the workflow, such as a spatial entropy map, provide a great tool for further uncertainty assessment and can be used to identify swept or unswept reservoir regions and the regions where more information is needed to reduce the uncertainty.
Bayesian inference and ultrasonic velocity have been used to estimate the self-association concen... more Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chains can cause an earlier onset of asphaltene self-association. Asphaltenes constitute the heaviest and most complicated fraction of crude petroleum and include a surface-active sub-fraction. When present above a critical concentration in pure solvent, asphaltene “monomers” self-associate and form nanoaggregates. Asphaltene nanoaggregates are thought to play a significant role during the remediation of petroleum spills and seeps. When mixed with water, petroleum becomes expensive to remove from the water column by conventional methods. The main reason of this difficulty is the presence of highly surface-active asphaltenes in petroleum. The nanoaggregates are thought to surround the water droplets, m...
Summary We present a data driven workflow to improve local history match quality by identifying m... more Summary We present a data driven workflow to improve local history match quality by identifying model regions from correlation between production response and geological modelling parameters for use in an assisted history matching framework. This paper outlines the implementation and results from a large mature field case study. Regions are identified by calculating the partial correlation between individual well production misfits and uncertain geological modelling parameters across 500 models. Wells are then categorised into three groups based on their correlations: positive, negative and insignificant. A probabilistic neural network (PNN) is trained on the location of each well and its group. A map of regions can then be calculated using the PNN. The parameters used to define the region map are then varied separately in each region in an assisted history matching loop. In the full field case study, an 8.8% improvement in oil rate misfit within the positively correlated well group was achieved by regional modification of the net-to-gross multiplier, with no detrimental effect on the other groups match quality. This case study demonstrates the effective identification and utilisation of geologically and dynamically inferred regions which improve the local history match
Abu Dhabi International Petroleum Exhibition and Conference, 2015
Reservoir modeling involves the characterization of the internal gridded petrophysical properties... more Reservoir modeling involves the characterization of the internal gridded petrophysical properties distribution and the simulation of fluid production. However, a common problem associated with reservoir modeling is the highly non-linear relationship between the distribution of the petrophysical parameters (frequently with a non-stationary character) and the fluid production. To tackle this problem, this paper presents a new methodology for integrated reservoir modeling by addressing a multiscale optimization approach applied for non-stationary geostatistical history matching of complex connectivity hydrocarbon reservoirs. The methodology comprises a two staged procedure for multiscale optimization, which includes a global optimization stage followed by a local refining stage. The former consists of optimizing the geological spatial anisotropy trend by coupling the stochastic optimization over the anisotropy multiparameter space with an image generation algorithm - direct sequential ...
This paper shows the challenges related to handling multiple training images for reservoir predic... more This paper shows the challenges related to handling multiple training images for reservoir prediction. We have identified two of the main challenges in handling multiple geological scenarios by creating a lower dimensional representation of the ensemble of model realizations: (i) how to relate geological knowledge to the metric space; and (ii) how to navigate in the metric space to facilitate in model update. In this work we demonstrate how to solve the classification problem in the metric space accounting for geological knowledge from a variety of prior geological concepts. In this paper we established geological relations in the metric space by making the links to the space of geologically interpretable parameters. These results would allow us to enhance geological realism of the new models obtained through the update process in the metric space.
Traditional geostatistical seismic inversion approaches are able to account for the uncertainty r... more Traditional geostatistical seismic inversion approaches are able to account for the uncertainty related with the stochastic simulation algorithms that are used as part of the inverse methodology for the model perturbation. However, they assume stationarity and no uncertainty related with large scale geological parameters represented for example by the spatial continuity pattern and the prior probability distribution of the property to invert as estimated from well-log data. We propose a multi-scale uncertainty assessment for traditional iterative geostatistical seismic methodologies by integrating stochastic adaptive sampling and Bayesian inference to tune the variogram ranges and the prior probability distribution of the property to invert within the inverse workflow. The application of the proposed methodology to a challenging synthetic dataset showed a good convergence of the inverted seismic towards the recorded one while the local and global uncertainty were jointly assessed.
Proceedings of the 32nd International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM) 2005), Tucson, USA, 30 March - 1 April 2005, 2005
Proceedings of SPE Annual Technical Conference and Exhibition, 2010
Reservoir modelling is frequently used in the oil industry to measure the risk associated with al... more Reservoir modelling is frequently used in the oil industry to measure the risk associated with alternative production scenarios. However, reservoir models themselves contain a high level of uncertainty because of the typically very limited, sparse and multi-scaled reservoir knowledge. The effect of this uncertainty can be assessed by producing a set of diverse models that match the production data reasonably well and using these models to quantify uncertainty in predicting the future performance of the reservoir. Evolutionary and swarm intelligence algorithms have become very popular for history matching due to their simplicity and parallel implementation capacity. This paper focuses on the application of Particle Swarm Optimisation (PSO) for history matching the Brugge field (a recent SPE benchmark case study). The parameterisation of the model is based on principal component analysis (PCA) for modelling spatially correlated random fields (e.g. porosity, net-to-gross and permeabili...
SPE Oil and Gas India Conference and Exhibition, 2010
History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncer... more History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncertainty in oil industry. Currently some innovative global optimisation approaches such as evolutionary algorithms and swarm intelligence methods have gained popularity for tackling history matching problems.Particle swarm optimisation (PSO) is a swarm intelligence approach for solving optimisation problems. In this approach particles are moving points in parameter space. The position of a particle is a candidate solution to the optimisation problem. Each particle searches for better positions in parameter space by updating its velocity according to rules originally inspired by behavioural models of the movement of flocks of birds.Recently the PSO algorithm has shown to be a promising tool for finding acceptable multiple history matched models quickly (Mohamed et al., 2009). However, the algorithm control parameters which balance between exploitation–exploration trade–off are to be studied...
ABSTRACT The focus of this paper is to demonstrate an integrated characterization approach for a ... more ABSTRACT The focus of this paper is to demonstrate an integrated characterization approach for a complex deltaic reservoir by using petrophysical properties and dynamic production data. Accurately characterizing deltaic reservoirs requires a non-stationary approach to the reservoir description. By linking the non-stationary characterization step with the dynamic flow modeling, the probability of success in locating infill wells increases. In this work, is presented a hybrid method which integrates the optimization firstly in the space of the anisotropy model parameters and secondly refines it in the space of the static models with a regional perturbation technique. It is an iterative methodology for optimized history matching, using adaptive stochastic sampling in the multiparameter space, and direct sequential simulation as the engine for the image transformation of the porosity and permeability models of the reservoir. The results show coherence when compared with the true case model. The approach provides the simultaneous simulation of the morphology and the property value.
ABSTRACT Geostatistical models reproduce spatial correlation based on the available on site data ... more ABSTRACT Geostatistical models reproduce spatial correlation based on the available on site data and more general concepts about the modelled patters, e.g. training images. One of the problem of modelling natural systems with geostatistics is in maintaining realism spatial features and so they agree with the physical processes in nature. Tuning the model parameters to the data may lead to geostatistical realisations with unrealistic spatial patterns, which would still honour the data. Such model would result in poor predictions, even though although fit the available data well. Conditioning the model to a wider range of relevant data provide a remedy that avoid producing unrealistic features in spatial models. For instance, there are vast amounts of information about the geometries of river channels that can be used in describing fluvial environment. Relations between the geometrical channel characteristics (width, depth, wave length, amplitude, etc.) are complex and non-parametric and are exhibit a great deal of uncertainty, which is important to propagate rigorously into the predictive model. These relations can be described within a Bayesian approach as multi-dimensional prior probability distributions. We propose a way to constrain multi-point statistics models with intelligent priors obtained from analysing a vast collection of contemporary river patterns based on previously published works. We applied machine learning techniques, namely neural networks and support vector machines, to extract multivariate non-parametric relations between geometrical characteristics of fluvial channels from the available data. An example demonstrates how ensuring geological realism helps to deliver more reliable prediction of a subsurface oil reservoir in a fluvial depositional environment.
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