Papers by Elias R Acosta
Nucleation and Atmospheric Aerosols, 2022
SPWLA 62nd Annual Online Symposium Transactions, May 17, 2021
This research proposed an alternative method for determining the saturation exponent (n) by findi... more This research proposed an alternative method for determining the saturation exponent (n) by finding the best correlations for the heterogeneity index using available core data and considering wettability changes. The log curves of the variable n were estimated, and the effect on the water saturation (Sw) calculations and the Stock Tank Oil Initially In Place (STOIIP) in the Tambaredjo (TAM) oil field was analyzed. Core data were employed to obtain the relationship between n and heterogeneity using cross-plots against several heterogeneity indices, reservoir properties, and pore throat size. After filtering the data, the clay volume (Vcl), shale volume, silt volume, basic petrophysical property index (BPPI), net reservoir index, pore grain volume ratio, and rock texture were defined as the best matches. Their modified/improved equations were applied to the log data and evaluated. The n related to Vcl was the best selection based on the criteria of depth variations and logical responses to the lithology. The Sw model in this field showed certain log readings (high resistivity [Rt] reading ≥ 500 ohm.m) that infer these intervals to be probable inverse-wet (oil-wet). The cross-plots (Rt vs. Vcl; Rt vs. density [RHOB]; Rt vs. total porosity [PHIT]) were used to discard the lithologies related to a high Rt (e.g., lignites and calcareous rocks) and to correct Sw when these resulted in values below the estimated irreducible water saturation (Swir). The Sw calculations using the Indonesian equation were updated to incorporate n as a variable (log curves), comparing it with Sw from the core data and previous calculations using a fixed average value (n = 1.82) from the core data. An integrated approach was used to determine n, which is related to the reservoir’s heterogeneity and wettability changes. The values of n for high Rt (n > 2) intervals ranged from 2.3 to 8.5, which is not close to the field average n value (1.82). Specific correlations were found by discriminating Swir (Swir < 15%), (Swir 15%–19%), and Swir (> 19%). The results showed that using n as a variable parameter improved Sw from 39.5% to 36.5% average in the T1 and T2 sands, showing a better fit than the core data average and increasing the STOIIP estimations by 6.81%. This represents now a primary oil recovery of 12.1%, closer to the expected value for these reservoirs. Although many studies have been done on n determination and its effect on Sw calculations, using average values over a whole field is still a common practice regardless of heterogeneity and wettability considerations. This study proposed a method to include the formation of heterogeneity and wettability changes in n determination, allowing a more reliable Sw determination as demonstrated in the TAM oil field in Suriname.
LACPEC 2020, Jul 20, 2020
The objective of this study was to characterize formation water resistivity (Rw) by validating wa... more The objective of this study was to characterize formation water resistivity (Rw) by validating water sample analyses, calibrating log derived Rw and mapping variations areal and vertically in order to reduce uncertainties regarding this property when determining water saturation (Sw) in the Tambaredjo oil field. Current calculations issued high Sw which originated low STOIIP and high actual oil recovery did not synchronize with the production of the field. A database with analyzed water sample and log-derived (Dielectric and Conventional logs) Rw data was created. Validation of the sample data was conducted by a Water Sample Analysis (WSA) tool, where the ionic balanced results were used. The log-derived Rw were normalized to the surface temperature and calibrated with the water sample analyzed Rw. To identify the initial Rw, groups of several wells (selected from the current produced water salinity maps) from total Tambaredjo Central Area (TCA) were made. With these initial Rw values the areal and reservoir distribution maps were determined and used for calculating the water saturation in the wells. Creating the WSA tool was very important to determine which samples were ionic balanced and were useful for further steps. These samples delivered different salinities which could clarify the water sources for the produced sand intervals. Three main groups of water sources were established based on the salinity; S-sands, T-sands and Cretaceous. Based on this classification, the log derived Rw were grouped and the salinities derived from the logs were compared with the produced water salinities retrieved from the Production Data Base. The log derived salinity that did not match the produced water salinity, the intervals from these logs were checked, re-selected and the salinity calculations were updated. Based on produced water salinity maps, well groups from the total TCA were created, comparing latest produced water salinity with initial ones. Assumptions were made of possible communications of water sources due to salinity values, where initial Rw did not match with the latest produced Rw. An initial Rw map was created and variable Rw proposed, instead of using a constant Rw for reservoir intervals of Tambaredjo oil field. Sw improves in all T-unit reservoirs of TCA from 41.9 to 40.4%, mainly in T1 and T2 sands (36.7% to 33.4%). In the T3 interval, Sw increases from 52.3 to 54.5% as formation water was found to be slightly fresher than originally estimated. Regarding stock tank oil initially in place (STOIIP) in TCA, with the variable Rw, it was estimated 265 MMSTB considering only T1 and T2 intervals. This is 17 MMSTB more than using the average value (Rw = 1.18ohm.m @ 67°F). The actual recovery is 14.2% and 14.9%, respectively, indicating that with the variable Rw, the actual recovery is closer to the expected primary recovery for this of field (10 to 13%) according to Ambastha (2008). Formation water salinity is a very important input in Sw equations. When geological features like top erosions, channel stacking or leaking seals are not tracked, and it is assumed that formation water resistivity is constant, water saturation calculations might not match production performances. This research proposes a methodology to identify sources of formation water mixture determining if a well was at initial reservoir conditions when it was logged, establishing Rw by areas.
Springer series in geomechanics and geoengineering, 2022
SPE Latin American and Caribbean Petroleum Engineering Conference
SPWLA 62nd Annual Online Symposium Transactions
This research proposed an alternative method for determining the saturation exponent (n) by findi... more This research proposed an alternative method for determining the saturation exponent (n) by finding the best correlations for the heterogeneity index using available core data and considering wettability changes. The log curves of the variable n were estimated, and the effect on the water saturation (Sw) calculations and the Stock Tank Oil Initially In Place (STOIIP) in the Tambaredjo (TAM) oil field was analyzed. Core data were employed to obtain the relationship between n and heterogeneity using cross-plots against several heterogeneity indices, reservoir properties, and pore throat size. After filtering the data, the clay volume (Vcl), shale volume, silt volume, basic petrophysical property index (BPPI), net reservoir index, pore grain volume ratio, and rock texture were defined as the best matches. Their modified/improved equations were applied to the log data and evaluated. The n related to Vcl was the best selection based on the criteria of depth variations and logical respons...
ICGPE, 2020
Some fields as Tambaredjo currently have a water saturation (Sw) model, which have not totally sa... more Some fields as Tambaredjo currently have a water saturation (Sw) model, which have not totally satisfied reservoir characterization in terms of initial fluid distribution. This mismatch has been observed in terms of low Stock Tank Oil Initially In Place (STOIIP) estimations and difficulties to reproduce production history during simulation modeling. The effect of shale on Sw calculations, when it is mainly composed of clay, is not an issue for determining the shale parameters. The challenge is when in the formation shale composition is distributed between clay and silt. This article proposes a method to estimate clay and silt parameters, incorporating both corrections into Sw calculations. Laser Particle Size Analyses (LPSA) and X-ray Diffraction (XRD) data were used to determine clay and silt distributions. Tortuosity (a) and cementation exponent (m) from core data were related to rock types using Pore Throat Size (PTS) distribution and then related to the Basic Petrophysics Properties Index (BPPI). Guided by the BPPI, a and m were extrapolated to clay and silt. To incorporate clay and silt corrections into Sw, reasoning from scratch was done. Instead of using a unique term for shale, clay and silt were separated and the deduction process continued including also formation water resistivity (Rw) for each term (sand, clay and silt). Laminar approach was also considered as it seems to fit better in certain areas of the field. The proposed equations were evaluated on well basis to verify the match with field production performance. Initial results show improvements from 41.9 to 19.9% in average Sw for the Center Area of the Tambaredjo field.
ICGPE , 2020
Determining the best equation for water saturation (Sw) can be challenging for some reservoirs. O... more Determining the best equation for water saturation (Sw) can be challenging for some reservoirs. Objection to the use of a certain equation may be because it was not originally conceived for the reservoir characteristics (unconsolidated and heavy oil) in the studied field and justification or support for it use may induce a broader study. This article proposes a method to assess in detail the Sw equations and determine the proper one for the studied field. Two very important steps were done in order to establish the Sw equation for the Tambaredjo field. First an inventory of all core data useful for calibrating the equations and second the creation of a tool, establishing criterion for assessment of equations. Core data with information of direct or indirect irreducible water saturation (Swir) was gathered. 27 samples with different tests like capillary pressure, mercury injection, resistivity index, nuclear magnetic resonance and relative permeability were used as calibration references for the different Sw equations (e.g. Indonesian, Dual Water, etc.). The Water Saturation Equation Assessment Tool (WSEAT) was created to assess the Sw equations, classifying them to select the best fit. The tool compares directly the results of each equation against the core sample. All sensitive variations in the inputs of the Sw equations were analyzed. Results confirmed that the Modified Indonesian is the best fit. Since this equation was created for Tar sands (not the case in the Tambaredjo field), further research was done by exploring the introduction of modifications, reviewing assumptions and propose solutions for input terms which are difficult to estimate. An equation was developed, the Staatsolie Suriname Clay Sw. This equation incorporates clay bound water resistivity which has been determined using dielectric logs. A laminar approach version of the equation was created considering that some core samples seem to have better fit to this approach. The proposed equations were evaluated on well basis to verify the match with field production performance. Results in the Tambaredjo Central Area of the field show improvements in Sw from 41.9 to 25.6%, that Stock Tank Oil Initially In Place (STOIIP) represents an increase of 17.43%. This expressed in terms of actual recovery (12.7%) represents a value more in accordance with the expected primary recovery for the field.
Researchgate, 2019
Correct estimation of the Archie parameters has always been challenging. Tortuosity, a and cement... more Correct estimation of the Archie parameters has always been challenging. Tortuosity, a and cementation factor, m are functions of changes in pore geometry, tortuosity of the pores, formation pressure and clay content. The m is not a constant, but is a variable depending on many physical parameters and lithological attributes of porous media. The fact that a and m values are codependent, a should not be considered a constant either. The purpose of this article is to present the estimated impact of using different values for the parameters tortuosity a and cementation factor, m for the heterogeneous T Sands formation of the Tambaredjo Field, using the core data of wells 1M10.1 and 9B11.1.
Heterogeneous formations vary in sizes, shapes and distribution of grains and pores. Selecting the correct heterogeneity index related to a and m values from core data should lead to better representation of changes in formation lithology. Choosing an appropriate index is critical and should be done with caution.
This research approaches the changes in formation lithology by using ranges for a and m values. First, a quality check analysis was done with well log data of the above mentioned wells, then petrophysical evaluation was done. Sensitivity analysis was executed on 4 Saturation models based on available core data ranges for a and m.
Depending on the location and depth at which a sample is taken within a heterogeneous reservoir, a and m will vary. Using fixed average, a & m values for an entire field can result in misleading calculations of the water saturation. The model giving the least difference in calculated water saturation compared to irreducible water saturation from core, is the Indonesian modified model by Woodhouse (1976), specially developed for Athabasca Tar Sands.
Water Dump Flooding is less known for revitalizing mature fields. However, in the Boca Field, spe... more Water Dump Flooding is less known for revitalizing mature fields. However, in the Boca Field, specifically Reservoir 95 Y-102, this is exactly what happened. Periodic review of this mature field had resulted in the suggestion to abandon the only producing well in this reservoir, well X-3, because an adjacent well, X-6, located above dip was known to produce with a 99% water cut. Although other wells were producing in the reservoir, only 12% recovery was realized. Therefore, an integrated study to re-evaluate the parameters and properties of Reservoir 95 Y-102 started in 2005. During the well analysis, it was found that water production of well X-6 was the result of the communication behind the casing of the well with the underlying aquifer 101 and not because of the advancement of the oil-water contact as initially suggested. Recompletion of well X-3 was recommended because an injection process known as dump flooding was underway. In addition, aquifer support of production over the previous 7 years strongly indicated that dump flood would produce the desired production increases. The accidental Water Dump Flooding, under sub-optimal conditions, actually rejuvenated the producing well, increasing production to more than 300 BOPD with acceptable water cut of 61%. Outlined below are the steps followed for the analysis and understanding of the process that occurred and how we took advantage of this accidental Dump Flooding, which raised the production from nearly zero to over 100,000 barrels produced in a single year. This lays the foundation for using Dump Flooding as a production development strategy for other projects in the area.
La Inyección Endógena de Agua (Water Dump Flooding) es menos conocida por revitalizar campos madu... more La Inyección Endógena de Agua (Water Dump Flooding) es menos conocida por revitalizar campos maduros. De cualquier manera, en el Campo Boca, específicamente en el Yacimiento 95 Y-102, esto fue exactamente lo que sucedió. Revisiones periódicas de este campo maduro resultó en la sugerencia de abandonar el único pozo productor en este yacimiento, el X-3, debido a que un pozo vecino, el X-6, localizado buzamiento arriba había probado 99% de corte de agua.
Aun cuando hubo otros pozos productores en el yacimiento, solo se había logrado el 12% del recobro. Por este motivo, comenzó en el año 2005 un estudio integrado para revaluar los parámetros y propiedades del Yacimiento 95 Y-102. Durante el análisis de pozos, se encontró que la producción de agua del pozo X-6 era el resultado de la comunicación por detrás del revestidor del pozo con el Acuífero 101, subyacente y no debido al avance del contacto agua-petróleo como fue sugerido inicialmente. Fue recomendada la recompletación del pozo X-3, ya que estaba ocurriendo un proceso de inyección conocido como inyección endógena (dump flooding). Adicionalmente, el soporte de la producción por el acuífero durante los 7 años previos indicaba fuertemente que la inyección endógena produciría los incrementos deseados.
La Inyección Endógena de Agua accidental, bajo condiciones sub-optimas, de hecho rejuveneció el pozo productor, incrementando la producción a más de 300 BND con un corte de agua aceptable de 61%. Abajo en términos generales se describen los pasos seguidos para el análisis y comprensión del proceso y de como se le sacó ventaja a esta inyección endógena accidental, que elevó la producción de casi cero a más de 100.000 barriles producidos en un solo año. Esto sentó las bases para el uso de la Inyección Endógena de Agua, como estrategia de desarrollo de producción para otros proyectos en el área.
For many years, it has been common practice to use tortuosity and cementation parameters as pre-e... more For many years, it has been common practice to use tortuosity and cementation parameters as pre-established values. Nevertheless, is the importance of these parameters really understood? Do we know the effect on water saturation calculations of these parameters? Over the years and having worked Petrophysics for different environments and variable data sets, have notice how careless or easy selected these parameters are determined. Many times based on the first set of wells of the field or just by analogy. Interesting was to find a series of technical papers and studies regarding this topic and how a variety of techniques and values are available. This paper will review the determinations of these parameters and research their effect on water saturation calculations.
Conference Presentations by Elias R Acosta
AAPG ICE, 2019
The Tambaredjo (TAM) oilfield is located in the marshy coastal area in the district of Saramacca,... more The Tambaredjo (TAM) oilfield is located in the marshy coastal area in the district of Saramacca, Suriname. The reservoirs in the TAM field are part of the unconsolidated Paleocene sands, the so-called T-sands (T1, T2, and T3 units at 900-1400ft depths), on lapping against the 1-2 degree sloping top of the Cretaceous unconformity. The T-Unit is very erratically deposited in an upper to lower delta plain with braided to meandering fluvial channels with shallow marine influences. This shallow field produces a medium-heavy oil (~ 16 °API gravity) with a reservoir viscosity of around 600 cP. Production commenced in 1982 and has peaked to nearly 17,500 BOPD, through drilling of over 2,000 production wells. Apparent production decline, increasing water cuts, and depleting pressure in this field, justify the need for secondary and
tertiary recovery techniques. Based on common screening criteria, part of the field is suitable for polymer flooding. Staatsolie initiated a pilot polymer flood project in September 2008 after simulations with a 3D geocellular model built with sand and no sand lithofacies approach. Forecasts resulting from dynamic modeling compared to the pilot results seemed pessimistic and unreliable due to uncertainties related to the lithofacies approach. High reservoir heterogeneity and lacking laboratory measurements for dynamic properties related to the lithofacies, resulted in inflow issues in the dynamic simulation model. A new sedimentological approach for 3D geocellular static modeling based on Rock Typing (RT) opposed to the conventional lithofacies modeling appears to capture the flow characteristics within the reservoir, since several flow units can be present in a single lithofacies. The Winland-Pittman method is based on an empirical equation relating porosity (storage capacity) and permeability (flow capacity) to a pore throat radius (PTR), which is used to tackle the facies issue. Based on this calculation the T-Unit reservoirs can be subdivided into 6 (six) flow units (rock facies). RT fraction maps were then used to guide stochastic trend and facies modeling processes. All property distributions (e.g. porosity, permeability, and water saturation) were conditioned to the Rock Type ‘facies’.
Based on the PTR, the rock types could be linked to the flow properties. This major change in modeling, better suits the end purpose of the static model: to provide a robust and accurate simulation flow model to predict polymer injection and its production response in the certain part of the TAM oilfield.
Presentation at:
http://www.searchanddiscovery.com/pdfz/documents/2019/51628jowintinie/ndx_jowintinie.pdf.html
IX INGEPET 2018 (EXPL-IR-ML-21-E), 2018
Este artículo compara los resultados de un proceso de inyección convencional de agua versus inyec... more Este artículo compara los resultados de un proceso de inyección convencional de agua versus inyección cíclica en un yacimiento de crudos pesados de 15 °API de la Formación Oficina en el campo San Cristóbal. La inyección cíclica de agua es una técnica de recuperación secundaria dividida en dos periodos: Inyección y Cierre de pozos inyectores, en este último ocurre despresurización y redistribución de fluidos y basados en la histéresis de las fuerzas capilares generadas en el sistema roca fluido en tal sentido algunas moléculas de petróleo se desplazan desde zonas de bajas propiedades hacia zonas de mejores características petrofísicas y en consecuencia hacia pozos productores, los cuales siempre se mantienen activos, permitiendo mayor recobro de petróleo y reducción del agua producida durante el medio ciclo de cierre de pozos inyectores. Ello se obtuvo a través de un estudio de simulación 3D Black Oil (Eclipse 100) activando la opción Histéresis, donde se calibró el modelo de roca fluido usando las curvas de drenaje e imbibición de la Presión Capilar obtenidas de análisis especiales de un núcleo del yacimiento en estudio, a fin de comparar ambos procesos.
Thesis Chapters by Elias R Acosta
En la Cuenca Oriental de Venezuela existen yacimientos productores de hidrocarburos con resistivi... more En la Cuenca Oriental de Venezuela existen yacimientos productores de hidrocarburos con resistividades muy bajas que presentan atractivos niveles de producción con muy bajo contenido de agua, que por
interpretaciones erradas en algún momento fueron señaladas como arenas de agua o con alto contenido de ésta, situación que obligó a plantear la integración de información de perfiles de pozos, análisis de núcleos y/o muestras de pared, datos de producción para definir parámetros petrofísicos en yacimientos con alta saturación irreducible de agua, desarrollando un nuevo modelo interpretativo para caracterizar
debidamente estos yacimientos y aumentar la producción de los campos maduros y nuevos del Distrito Social San Tomé. El estudio comenzó con la recopilación y validación de la información disponible en los campos: Chimire, Yopales Central, Trico, Boca y Budare; campos en los cuales se han probado arenas con la problemática planteada en este estudio. Para la selección definitiva de los yacimientos a estudiar se analizó una lista preliminar de 15 yacimientos, seleccionándose 9 por tener las mejores características para esta investigación: baja resistividad (alta saturación irreducible de agua) en algún pozo que produjo petróleo y espesores delgados. Los yacimientos estudiados son: D2U OM-354, E1 OM-304, E1 OM-386 y M1 NS-301 del Campo Chimire; K OM-204 y N2 OG-286 del Campo Trico; N1 YS-66 del Campo
Yopales Central y R4U BDV-13 de Campo Budare. Se determinaron los parámetros petrofísicos. Posteriormente se establecieron los modelos de arcillosidad, porosidad, saturación de agua y permeabilidad; lográndose cuatro nuevos modelos de saturación de agua, denominadas Acosta & Rosales para la Formación Oficina e identificando la conveniencia del modelo de Smit para la permeabilidad. Se logró integrar los resultados petrofísicos con la litología, sedimentología y producción, obteniéndose finalmente un modelo integrado para los yacimientos analizados.
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Papers by Elias R Acosta
Heterogeneous formations vary in sizes, shapes and distribution of grains and pores. Selecting the correct heterogeneity index related to a and m values from core data should lead to better representation of changes in formation lithology. Choosing an appropriate index is critical and should be done with caution.
This research approaches the changes in formation lithology by using ranges for a and m values. First, a quality check analysis was done with well log data of the above mentioned wells, then petrophysical evaluation was done. Sensitivity analysis was executed on 4 Saturation models based on available core data ranges for a and m.
Depending on the location and depth at which a sample is taken within a heterogeneous reservoir, a and m will vary. Using fixed average, a & m values for an entire field can result in misleading calculations of the water saturation. The model giving the least difference in calculated water saturation compared to irreducible water saturation from core, is the Indonesian modified model by Woodhouse (1976), specially developed for Athabasca Tar Sands.
Aun cuando hubo otros pozos productores en el yacimiento, solo se había logrado el 12% del recobro. Por este motivo, comenzó en el año 2005 un estudio integrado para revaluar los parámetros y propiedades del Yacimiento 95 Y-102. Durante el análisis de pozos, se encontró que la producción de agua del pozo X-6 era el resultado de la comunicación por detrás del revestidor del pozo con el Acuífero 101, subyacente y no debido al avance del contacto agua-petróleo como fue sugerido inicialmente. Fue recomendada la recompletación del pozo X-3, ya que estaba ocurriendo un proceso de inyección conocido como inyección endógena (dump flooding). Adicionalmente, el soporte de la producción por el acuífero durante los 7 años previos indicaba fuertemente que la inyección endógena produciría los incrementos deseados.
La Inyección Endógena de Agua accidental, bajo condiciones sub-optimas, de hecho rejuveneció el pozo productor, incrementando la producción a más de 300 BND con un corte de agua aceptable de 61%. Abajo en términos generales se describen los pasos seguidos para el análisis y comprensión del proceso y de como se le sacó ventaja a esta inyección endógena accidental, que elevó la producción de casi cero a más de 100.000 barriles producidos en un solo año. Esto sentó las bases para el uso de la Inyección Endógena de Agua, como estrategia de desarrollo de producción para otros proyectos en el área.
Conference Presentations by Elias R Acosta
tertiary recovery techniques. Based on common screening criteria, part of the field is suitable for polymer flooding. Staatsolie initiated a pilot polymer flood project in September 2008 after simulations with a 3D geocellular model built with sand and no sand lithofacies approach. Forecasts resulting from dynamic modeling compared to the pilot results seemed pessimistic and unreliable due to uncertainties related to the lithofacies approach. High reservoir heterogeneity and lacking laboratory measurements for dynamic properties related to the lithofacies, resulted in inflow issues in the dynamic simulation model. A new sedimentological approach for 3D geocellular static modeling based on Rock Typing (RT) opposed to the conventional lithofacies modeling appears to capture the flow characteristics within the reservoir, since several flow units can be present in a single lithofacies. The Winland-Pittman method is based on an empirical equation relating porosity (storage capacity) and permeability (flow capacity) to a pore throat radius (PTR), which is used to tackle the facies issue. Based on this calculation the T-Unit reservoirs can be subdivided into 6 (six) flow units (rock facies). RT fraction maps were then used to guide stochastic trend and facies modeling processes. All property distributions (e.g. porosity, permeability, and water saturation) were conditioned to the Rock Type ‘facies’.
Based on the PTR, the rock types could be linked to the flow properties. This major change in modeling, better suits the end purpose of the static model: to provide a robust and accurate simulation flow model to predict polymer injection and its production response in the certain part of the TAM oilfield.
Presentation at:
http://www.searchanddiscovery.com/pdfz/documents/2019/51628jowintinie/ndx_jowintinie.pdf.html
Thesis Chapters by Elias R Acosta
interpretaciones erradas en algún momento fueron señaladas como arenas de agua o con alto contenido de ésta, situación que obligó a plantear la integración de información de perfiles de pozos, análisis de núcleos y/o muestras de pared, datos de producción para definir parámetros petrofísicos en yacimientos con alta saturación irreducible de agua, desarrollando un nuevo modelo interpretativo para caracterizar
debidamente estos yacimientos y aumentar la producción de los campos maduros y nuevos del Distrito Social San Tomé. El estudio comenzó con la recopilación y validación de la información disponible en los campos: Chimire, Yopales Central, Trico, Boca y Budare; campos en los cuales se han probado arenas con la problemática planteada en este estudio. Para la selección definitiva de los yacimientos a estudiar se analizó una lista preliminar de 15 yacimientos, seleccionándose 9 por tener las mejores características para esta investigación: baja resistividad (alta saturación irreducible de agua) en algún pozo que produjo petróleo y espesores delgados. Los yacimientos estudiados son: D2U OM-354, E1 OM-304, E1 OM-386 y M1 NS-301 del Campo Chimire; K OM-204 y N2 OG-286 del Campo Trico; N1 YS-66 del Campo
Yopales Central y R4U BDV-13 de Campo Budare. Se determinaron los parámetros petrofísicos. Posteriormente se establecieron los modelos de arcillosidad, porosidad, saturación de agua y permeabilidad; lográndose cuatro nuevos modelos de saturación de agua, denominadas Acosta & Rosales para la Formación Oficina e identificando la conveniencia del modelo de Smit para la permeabilidad. Se logró integrar los resultados petrofísicos con la litología, sedimentología y producción, obteniéndose finalmente un modelo integrado para los yacimientos analizados.
Heterogeneous formations vary in sizes, shapes and distribution of grains and pores. Selecting the correct heterogeneity index related to a and m values from core data should lead to better representation of changes in formation lithology. Choosing an appropriate index is critical and should be done with caution.
This research approaches the changes in formation lithology by using ranges for a and m values. First, a quality check analysis was done with well log data of the above mentioned wells, then petrophysical evaluation was done. Sensitivity analysis was executed on 4 Saturation models based on available core data ranges for a and m.
Depending on the location and depth at which a sample is taken within a heterogeneous reservoir, a and m will vary. Using fixed average, a & m values for an entire field can result in misleading calculations of the water saturation. The model giving the least difference in calculated water saturation compared to irreducible water saturation from core, is the Indonesian modified model by Woodhouse (1976), specially developed for Athabasca Tar Sands.
Aun cuando hubo otros pozos productores en el yacimiento, solo se había logrado el 12% del recobro. Por este motivo, comenzó en el año 2005 un estudio integrado para revaluar los parámetros y propiedades del Yacimiento 95 Y-102. Durante el análisis de pozos, se encontró que la producción de agua del pozo X-6 era el resultado de la comunicación por detrás del revestidor del pozo con el Acuífero 101, subyacente y no debido al avance del contacto agua-petróleo como fue sugerido inicialmente. Fue recomendada la recompletación del pozo X-3, ya que estaba ocurriendo un proceso de inyección conocido como inyección endógena (dump flooding). Adicionalmente, el soporte de la producción por el acuífero durante los 7 años previos indicaba fuertemente que la inyección endógena produciría los incrementos deseados.
La Inyección Endógena de Agua accidental, bajo condiciones sub-optimas, de hecho rejuveneció el pozo productor, incrementando la producción a más de 300 BND con un corte de agua aceptable de 61%. Abajo en términos generales se describen los pasos seguidos para el análisis y comprensión del proceso y de como se le sacó ventaja a esta inyección endógena accidental, que elevó la producción de casi cero a más de 100.000 barriles producidos en un solo año. Esto sentó las bases para el uso de la Inyección Endógena de Agua, como estrategia de desarrollo de producción para otros proyectos en el área.
tertiary recovery techniques. Based on common screening criteria, part of the field is suitable for polymer flooding. Staatsolie initiated a pilot polymer flood project in September 2008 after simulations with a 3D geocellular model built with sand and no sand lithofacies approach. Forecasts resulting from dynamic modeling compared to the pilot results seemed pessimistic and unreliable due to uncertainties related to the lithofacies approach. High reservoir heterogeneity and lacking laboratory measurements for dynamic properties related to the lithofacies, resulted in inflow issues in the dynamic simulation model. A new sedimentological approach for 3D geocellular static modeling based on Rock Typing (RT) opposed to the conventional lithofacies modeling appears to capture the flow characteristics within the reservoir, since several flow units can be present in a single lithofacies. The Winland-Pittman method is based on an empirical equation relating porosity (storage capacity) and permeability (flow capacity) to a pore throat radius (PTR), which is used to tackle the facies issue. Based on this calculation the T-Unit reservoirs can be subdivided into 6 (six) flow units (rock facies). RT fraction maps were then used to guide stochastic trend and facies modeling processes. All property distributions (e.g. porosity, permeability, and water saturation) were conditioned to the Rock Type ‘facies’.
Based on the PTR, the rock types could be linked to the flow properties. This major change in modeling, better suits the end purpose of the static model: to provide a robust and accurate simulation flow model to predict polymer injection and its production response in the certain part of the TAM oilfield.
Presentation at:
http://www.searchanddiscovery.com/pdfz/documents/2019/51628jowintinie/ndx_jowintinie.pdf.html
interpretaciones erradas en algún momento fueron señaladas como arenas de agua o con alto contenido de ésta, situación que obligó a plantear la integración de información de perfiles de pozos, análisis de núcleos y/o muestras de pared, datos de producción para definir parámetros petrofísicos en yacimientos con alta saturación irreducible de agua, desarrollando un nuevo modelo interpretativo para caracterizar
debidamente estos yacimientos y aumentar la producción de los campos maduros y nuevos del Distrito Social San Tomé. El estudio comenzó con la recopilación y validación de la información disponible en los campos: Chimire, Yopales Central, Trico, Boca y Budare; campos en los cuales se han probado arenas con la problemática planteada en este estudio. Para la selección definitiva de los yacimientos a estudiar se analizó una lista preliminar de 15 yacimientos, seleccionándose 9 por tener las mejores características para esta investigación: baja resistividad (alta saturación irreducible de agua) en algún pozo que produjo petróleo y espesores delgados. Los yacimientos estudiados son: D2U OM-354, E1 OM-304, E1 OM-386 y M1 NS-301 del Campo Chimire; K OM-204 y N2 OG-286 del Campo Trico; N1 YS-66 del Campo
Yopales Central y R4U BDV-13 de Campo Budare. Se determinaron los parámetros petrofísicos. Posteriormente se establecieron los modelos de arcillosidad, porosidad, saturación de agua y permeabilidad; lográndose cuatro nuevos modelos de saturación de agua, denominadas Acosta & Rosales para la Formación Oficina e identificando la conveniencia del modelo de Smit para la permeabilidad. Se logró integrar los resultados petrofísicos con la litología, sedimentología y producción, obteniéndose finalmente un modelo integrado para los yacimientos analizados.