M. Al-Azmi, F.B. Al-Otaibi, J. G. Kumar, D. Tiwary, Samar Al-Ashwak, Bekdaulet Dzhaykiev, Neha Shinde, Douglas L. Hardman, Rabih Noueihed, S. Gadkari
The complex nature of the reservoir dictated comprehensive formation evaluation logging that was typically done on wireline. The high angle designed for maximum reservoir exposure, high temperature, high pressure (HTHP), differential reservoir pressure and wellbore stability challenges necessitated a new approach to overall formation evaluation. The paper outlines Formation Evaluation strategy that reduced risk, increased efficiency and saved money, while ensuring high quality data collection, integration and interpretation. After review of all risks, a decision to utilize Managed Pressure Drilling (MPD) for wellbore stability, Logging While Drilling (LWD) to replace wireline and Advanced Mudlogging Services was implemented. The Formation Evaluation team utilized LWD resistivity, neutron, density and nuclear magnetic resonance logs supplemented with x-ray diffraction (XRD), x-ray fluorescence (XRF) and advanced mud gas analysis to ensure comprehensive analysis. The paper outlines workflows and procedures necessary to ensure all data from LWD, XRF, XRD and mud gas are integrated properly for the analysis. Effects of Managed Pressure Drilling on mud gas interpretation as well as cuttings and mud gas depth matching are addressed. Depth matching of all data, mud gasses, cuttings and logs are critical for detailed and accurate analysis and techniques are discussed that ensure consistent results. Complex mineralogy due to digenesis and effect of LWD logs are evident and only reconciled by detailed XRF and XRD data. The effects of some conductive mineralogy are so dramatic as to infer tool function compromise. The ability to determine acceptable tool response from tool failures eliminates unnecessary trips and leads to efficient operations. The final result of the above data collection, QC and processing resulted in a comprehensive formation evaluation interpretation of high confidence. Finally, conclusions and recommendations are summarized to provide guidelines in Formation Evaluation in similar challenging highly deviated, HTHP, complex reservoir environments on land and offshore.
{"title":"Overcoming Formation Evaluation Challenges in Highly Deviated Jurassic Wells with LWD and Advanced Mud Logging Services","authors":"M. Al-Azmi, F.B. Al-Otaibi, J. G. Kumar, D. Tiwary, Samar Al-Ashwak, Bekdaulet Dzhaykiev, Neha Shinde, Douglas L. Hardman, Rabih Noueihed, S. Gadkari","doi":"10.2118/196714-ms","DOIUrl":"https://doi.org/10.2118/196714-ms","url":null,"abstract":"\u0000 The complex nature of the reservoir dictated comprehensive formation evaluation logging that was typically done on wireline. The high angle designed for maximum reservoir exposure, high temperature, high pressure (HTHP), differential reservoir pressure and wellbore stability challenges necessitated a new approach to overall formation evaluation. The paper outlines Formation Evaluation strategy that reduced risk, increased efficiency and saved money, while ensuring high quality data collection, integration and interpretation.\u0000 After review of all risks, a decision to utilize Managed Pressure Drilling (MPD) for wellbore stability, Logging While Drilling (LWD) to replace wireline and Advanced Mudlogging Services was implemented. The Formation Evaluation team utilized LWD resistivity, neutron, density and nuclear magnetic resonance logs supplemented with x-ray diffraction (XRD), x-ray fluorescence (XRF) and advanced mud gas analysis to ensure comprehensive analysis. The paper outlines workflows and procedures necessary to ensure all data from LWD, XRF, XRD and mud gas are integrated properly for the analysis.\u0000 Effects of Managed Pressure Drilling on mud gas interpretation as well as cuttings and mud gas depth matching are addressed. Depth matching of all data, mud gasses, cuttings and logs are critical for detailed and accurate analysis and techniques are discussed that ensure consistent results. Complex mineralogy due to digenesis and effect of LWD logs are evident and only reconciled by detailed XRF and XRD data. The effects of some conductive mineralogy are so dramatic as to infer tool function compromise. The ability to determine acceptable tool response from tool failures eliminates unnecessary trips and leads to efficient operations. The final result of the above data collection, QC and processing resulted in a comprehensive formation evaluation interpretation of high confidence.\u0000 Finally, conclusions and recommendations are summarized to provide guidelines in Formation Evaluation in similar challenging highly deviated, HTHP, complex reservoir environments on land and offshore.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129689139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qian Sun, N. Zhang, Nayef Alyafei, Yuhe Wang, M. Fadlelmula
Reservoir simulation is commonly performed on upscaled models of complex geological models. The upscaling process introduces a principal challenge in accurately simulating two-phase fluid dynamics in porous media. To tackle this challenge, it is important to upscale relative permeability accurately. In this paper, a numerical method, which is based on the mimetic finite difference method (MFD) and digital rock analysis (DRA), is proposed for relative permeability upscaling. The validation of MFD is tested by two different cases with exact pressure solution. Then, the relative permeability of the digital rock (small element) is calculated based on the pore network modeling. The small elements are combined together to make up a larger model with different sizes (4×4×4, 6×6×6, 8×8×8, 10×10×10 elements). Finally, the accuracy of the proposed method is verified by comparing simulated results of the different sizes with that of the original one. The results show that MFD can solve the multi-phase flow scenarios with high accuracy and the L2 error follows the opposite trend to that of mesh size, which means that more refinement level gives less L2 error. For the upscaling of absolute permeability, the relative error can be decreased to 2.27%, which confirms that the proposed method is capable of calculating the absolute permeability with higher refinement levels. The fitting degree of the simulated water phase relative permeability to the original one is better than that of oil phase. The average relative error of water pahse relative permeability upscaling can decrease to less than 5.0%. It is found that the results will get worse when the model includes less elements. Especially at low water saturation, there exists some fluctuations for relative permeability curves and it may be due to the unstable state of the waterflood front with less elements involved.
{"title":"Numerical Relative Permeability Upscaling Based on Digital Rock Analysis","authors":"Qian Sun, N. Zhang, Nayef Alyafei, Yuhe Wang, M. Fadlelmula","doi":"10.2118/196687-ms","DOIUrl":"https://doi.org/10.2118/196687-ms","url":null,"abstract":"\u0000 Reservoir simulation is commonly performed on upscaled models of complex geological models. The upscaling process introduces a principal challenge in accurately simulating two-phase fluid dynamics in porous media. To tackle this challenge, it is important to upscale relative permeability accurately. In this paper, a numerical method, which is based on the mimetic finite difference method (MFD) and digital rock analysis (DRA), is proposed for relative permeability upscaling. The validation of MFD is tested by two different cases with exact pressure solution. Then, the relative permeability of the digital rock (small element) is calculated based on the pore network modeling. The small elements are combined together to make up a larger model with different sizes (4×4×4, 6×6×6, 8×8×8, 10×10×10 elements). Finally, the accuracy of the proposed method is verified by comparing simulated results of the different sizes with that of the original one. The results show that MFD can solve the multi-phase flow scenarios with high accuracy and the L2 error follows the opposite trend to that of mesh size, which means that more refinement level gives less L2 error. For the upscaling of absolute permeability, the relative error can be decreased to 2.27%, which confirms that the proposed method is capable of calculating the absolute permeability with higher refinement levels. The fitting degree of the simulated water phase relative permeability to the original one is better than that of oil phase. The average relative error of water pahse relative permeability upscaling can decrease to less than 5.0%. It is found that the results will get worse when the model includes less elements. Especially at low water saturation, there exists some fluctuations for relative permeability curves and it may be due to the unstable state of the waterflood front with less elements involved.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134278106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents a new open access carbonate reservoir case study that uniquely considers the major uncertainties inherent to carbonate reservoirs using one of the most prolific aggradational parasequence carbonate formation set in the U.A.E; the Late Barremian Upper Kharaib Mb. as an analogue. The ensemble considers a range of interpretational scenarios and geomodelling techniques to capture the main components of its reservoir architectures, stratal geometries, facies, pore systems, diagenetic overprints and wettability variations across its platform-to-basin profile. Fully anonymized data from 43 wells across 22 fields in the Bab Basin, U.A.E from different geo-depositional positions and height above FWL’s (specified to capture multiple structural positions) within an area of 36,000 km2 was used. The data comprises of a full suite of open hole logs and core data which has been anonymized, rescaled, repositioned and structurally deformed; FWL’s were normalized and the entire model was placed in a unique coordinate system. Our petrophysical model captures the geological setting and reservoir heterogeneities of selected fields but now at a manageable scale. The novelty of this work has been to create semi-synthetic open access carbonate reservoir models which enable the geoscience and reservoir engineering community to analyse, study and test number of cases related to new numerical algorithms for reservoir characterisation, reservoir simulation, uncertainty quantification, robust optimization and machine learning. The value of this study is also to expose a model and a dataset to the reservoir simulation engineers so they can explore the impact of different fluid flow physics on sweep and recovery across multiple carbonate reservoir architectures with diverse lateral and vertical rock and fluid complexities – all of which can be history-matched against a ‘truth case’.
{"title":"An Open Access Carbonate Reservoir Benchmarking Study for Reservoir Characterisation, Uncertainty Quantification & History Matching","authors":"J. Gomes, S. Geiger, D. Arnold","doi":"10.2118/196674-ms","DOIUrl":"https://doi.org/10.2118/196674-ms","url":null,"abstract":"\u0000 This work presents a new open access carbonate reservoir case study that uniquely considers the major uncertainties inherent to carbonate reservoirs using one of the most prolific aggradational parasequence carbonate formation set in the U.A.E; the Late Barremian Upper Kharaib Mb. as an analogue. The ensemble considers a range of interpretational scenarios and geomodelling techniques to capture the main components of its reservoir architectures, stratal geometries, facies, pore systems, diagenetic overprints and wettability variations across its platform-to-basin profile.\u0000 Fully anonymized data from 43 wells across 22 fields in the Bab Basin, U.A.E from different geo-depositional positions and height above FWL’s (specified to capture multiple structural positions) within an area of 36,000 km2 was used. The data comprises of a full suite of open hole logs and core data which has been anonymized, rescaled, repositioned and structurally deformed; FWL’s were normalized and the entire model was placed in a unique coordinate system. Our petrophysical model captures the geological setting and reservoir heterogeneities of selected fields but now at a manageable scale.\u0000 The novelty of this work has been to create semi-synthetic open access carbonate reservoir models which enable the geoscience and reservoir engineering community to analyse, study and test number of cases related to new numerical algorithms for reservoir characterisation, reservoir simulation, uncertainty quantification, robust optimization and machine learning. The value of this study is also to expose a model and a dataset to the reservoir simulation engineers so they can explore the impact of different fluid flow physics on sweep and recovery across multiple carbonate reservoir architectures with diverse lateral and vertical rock and fluid complexities – all of which can be history-matched against a ‘truth case’.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133039753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paolo Rizzato, D. Castano, L. Moghadasi, D. Renna, P. Pisicchio, M. Bartosek, Yohan Suhardiman, A. Maxwell
This paper describes the results of an integrated reservoir study aimed at producing hydrocarbons through a sustainable development from a green High Temperature (HT) giant CO2-rich gas field in the Australian offshore. The development concept addressed the complex challenge of exploiting resources while minimizing the carbon impact. In order to characterize the reservoir in the most detailed way and to describe the fluids behaviour, a 1.8 million active cells compositional model has been built. An analytical aquifer has been coupled in order to represent the boundary conditions of the area. The faults system, interpreted on seismic data by geophysicists, has been included in the simulation model. The selected development plan includes the re-injection of the produced CO2 into the aquifer of the reservoir itself. The supercritical CO2-brine relative permeability curves at reservoir conditions have been provided by Eni laboratories, where the experiments were performed. Therefore, a detailed model has been built with the purpose of: –Defining producing well and CO2 injector well locations, numbers and phasing to evaluate expected CO2 injectivity and CO2 breakthrough issues;–Optimizing the development concept through a risk analysis approach;–Estimating the CO2-rich gas injectivity and storage capacity in the saline aquifer of the reservoir;–Predicting the behavior of the CO2-rich gas after re-injection (breakthrough timing and plume migration);–Maximizing the CO2 sequestration in the reservoir.
{"title":"Dynamic Modeling of a High Temperature CO2-Rich Giant Gas Field with a Carbon Capture and Storage Strategy","authors":"Paolo Rizzato, D. Castano, L. Moghadasi, D. Renna, P. Pisicchio, M. Bartosek, Yohan Suhardiman, A. Maxwell","doi":"10.2118/196691-ms","DOIUrl":"https://doi.org/10.2118/196691-ms","url":null,"abstract":"\u0000 This paper describes the results of an integrated reservoir study aimed at producing hydrocarbons through a sustainable development from a green High Temperature (HT) giant CO2-rich gas field in the Australian offshore. The development concept addressed the complex challenge of exploiting resources while minimizing the carbon impact.\u0000 In order to characterize the reservoir in the most detailed way and to describe the fluids behaviour, a 1.8 million active cells compositional model has been built. An analytical aquifer has been coupled in order to represent the boundary conditions of the area.\u0000 The faults system, interpreted on seismic data by geophysicists, has been included in the simulation model. The selected development plan includes the re-injection of the produced CO2 into the aquifer of the reservoir itself. The supercritical CO2-brine relative permeability curves at reservoir conditions have been provided by Eni laboratories, where the experiments were performed.\u0000 Therefore, a detailed model has been built with the purpose of: –Defining producing well and CO2 injector well locations, numbers and phasing to evaluate expected CO2 injectivity and CO2 breakthrough issues;–Optimizing the development concept through a risk analysis approach;–Estimating the CO2-rich gas injectivity and storage capacity in the saline aquifer of the reservoir;–Predicting the behavior of the CO2-rich gas after re-injection (breakthrough timing and plume migration);–Maximizing the CO2 sequestration in the reservoir.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133718844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Maiorano, Pietro Selvaggio, Ripalta Eleonora Distaso, R. Rossi, E. Stano
The objective of this work is the prediction of water salinity evolution trend for Mexico Area-1 development that foresees the injection of a mixture of seawater and produced water from the six different reservoirs connected to the same FPSO. Prediction of salinity trend evolution is crucial for forecasting possible biogenic hydrogen sulphide (H2S) formation and foreseeing the relating impacts over completion and facility material selection and on health, safety and environment (HSE) management. Traditional numerical simulations through stand-alone models do not consider the effects of the reciprocal interaction among the fields on production profiles and are not able to simulate salinity evolution of produced and injected water mixture, variable over time. To overcome this limit, a new tool was developed. It consists in a python script that, introduced into the Area-1 Integrated Asset Model, allowed to generate forecasts of the water salinity along the project lifetime. These simulations were essential for souring risk assessment, providing the following results: water salinity trend evolution at each injector well;water salinity trend evolution at each producer well;injection water breakthrough timing at the producer wells. Moreover, it gave the opportunity to assess the injection strategy efficiency and to quantify the impact of changing salinity on water viscosity and on the field recovery. In conclusion, the innovative methodology applied in the Area-1 IAM (Integrated Asset Model) permits to predict the salinity of injected water and to foresee salinity evolution of produced water generating several valuable information, providing a flexible tool that allows to investigate simultaneously several uncertainties related to the project and to evaluate promptly solutions and mitigation. Moreover, when the reservoirs will be on production, the numerical models integrated with the developed script will reproduce the historical salinity data allowing to identify preferential flow path established by fluids virtually acting as a reservoir tracer technology.
{"title":"Innovative Water Salinity Management Through Integrated Asset Model Applied to Mexico Area-1","authors":"S. Maiorano, Pietro Selvaggio, Ripalta Eleonora Distaso, R. Rossi, E. Stano","doi":"10.2118/196622-ms","DOIUrl":"https://doi.org/10.2118/196622-ms","url":null,"abstract":"\u0000 The objective of this work is the prediction of water salinity evolution trend for Mexico Area-1 development that foresees the injection of a mixture of seawater and produced water from the six different reservoirs connected to the same FPSO.\u0000 Prediction of salinity trend evolution is crucial for forecasting possible biogenic hydrogen sulphide (H2S) formation and foreseeing the relating impacts over completion and facility material selection and on health, safety and environment (HSE) management.\u0000 Traditional numerical simulations through stand-alone models do not consider the effects of the reciprocal interaction among the fields on production profiles and are not able to simulate salinity evolution of produced and injected water mixture, variable over time. To overcome this limit, a new tool was developed. It consists in a python script that, introduced into the Area-1 Integrated Asset Model, allowed to generate forecasts of the water salinity along the project lifetime. These simulations were essential for souring risk assessment, providing the following results: water salinity trend evolution at each injector well;water salinity trend evolution at each producer well;injection water breakthrough timing at the producer wells.\u0000 Moreover, it gave the opportunity to assess the injection strategy efficiency and to quantify the impact of changing salinity on water viscosity and on the field recovery.\u0000 In conclusion, the innovative methodology applied in the Area-1 IAM (Integrated Asset Model) permits to predict the salinity of injected water and to foresee salinity evolution of produced water generating several valuable information, providing a flexible tool that allows to investigate simultaneously several uncertainties related to the project and to evaluate promptly solutions and mitigation.\u0000 Moreover, when the reservoirs will be on production, the numerical models integrated with the developed script will reproduce the historical salinity data allowing to identify preferential flow path established by fluids virtually acting as a reservoir tracer technology.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116533403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reservoir simulation optimization under uncertainty typically invokes a sense of anxiety mainly because of a lack of a systematic criterion to choose between different development scenarios under uncertainty, how to go about doing well placement and optimizing well controls in the face of a large uncertainty ensemble of static realisations, and most of all the large number of simulation runs that potentially needs to be conducted. This is exacerbated when the models are large and require many hours to run. Moreover, even with the prevalence of distributed and parallel computing clusters, there is still a limited amount of computing resources available when spread out over the number of reservoir engineers within a company. Time and budget constraints also contribute to complicating this process. Furthermore, with the requirement of an inordinately large number of simulation runs comes the dilemma as to which optimizer to choose that would help speed up the process. This paper first starts off with a brief background into historical attempts at tackling this problem by delving into the literature. Then it discusses a rigorous criterion for optimization under uncertainty viz. stochastic dominance, hitherto little known or used in the industry. A commonly used greenfield case study which is an ensemble set of uncertainty realisations is then introduced, which the rest of the paper will be based on. The ensemble is a pre-generated set of fifty realisations designed specifically for this problem. Two challenging areas will then be addressed viz. well placement optmisation under uncertainty, and well controls optimization under uncertainty. Finally, a comparison between the simplex, proxy response surface, differential evolution and particle swarm optimization methods is made in the optimization of well controls. Hence the paper aims to give a complete picture on how to go about reservoir simulation optimization under uncertainty, with a drastically reduced amount of computational runs that needs to be conducted. Practical and sensible formulation of the optimization problemcan go a long way to making this process more understandable and easier to implement.
{"title":"A Pragmatic Approach to Reservoir Simulation Optimisation Under Uncertainty","authors":"M. Kathrada, Khairul Azri","doi":"10.2118/196659-ms","DOIUrl":"https://doi.org/10.2118/196659-ms","url":null,"abstract":"\u0000 Reservoir simulation optimization under uncertainty typically invokes a sense of anxiety mainly because of a lack of a systematic criterion to choose between different development scenarios under uncertainty, how to go about doing well placement and optimizing well controls in the face of a large uncertainty ensemble of static realisations, and most of all the large number of simulation runs that potentially needs to be conducted. This is exacerbated when the models are large and require many hours to run. Moreover, even with the prevalence of distributed and parallel computing clusters, there is still a limited amount of computing resources available when spread out over the number of reservoir engineers within a company. Time and budget constraints also contribute to complicating this process. Furthermore, with the requirement of an inordinately large number of simulation runs comes the dilemma as to which optimizer to choose that would help speed up the process.\u0000 This paper first starts off with a brief background into historical attempts at tackling this problem by delving into the literature. Then it discusses a rigorous criterion for optimization under uncertainty viz. stochastic dominance, hitherto little known or used in the industry. A commonly used greenfield case study which is an ensemble set of uncertainty realisations is then introduced, which the rest of the paper will be based on. The ensemble is a pre-generated set of fifty realisations designed specifically for this problem. Two challenging areas will then be addressed viz. well placement optmisation under uncertainty, and well controls optimization under uncertainty.\u0000 Finally, a comparison between the simplex, proxy response surface, differential evolution and particle swarm optimization methods is made in the optimization of well controls. Hence the paper aims to give a complete picture on how to go about reservoir simulation optimization under uncertainty, with a drastically reduced amount of computational runs that needs to be conducted. Practical and sensible formulation of the optimization problemcan go a long way to making this process more understandable and easier to implement.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115357094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Bona, D. Santonico, Saida Machicote, A. Battigelli
For oil and gas companies, accelerating the time to first hydrocarbon is a strategic objective. Special core analysis programs for tight gas reservoirs may take many months because of the long equilibration times involved in the tests. This represents a bottleneck for achieving the goal of reducing the time-to- market. Both log interpretation and reservoir modelling activities are impacted by the long SCAL durations. In order to face the challenge, a suite of fast methods have been developed. They are fast because they operate under non-equilibrium conditions. The methods give the m&n parameters for electric log interpretation, the endpoint gas relative permeability and the relationship linking initial gas saturation, trapped gas saturation and endpoint water relative permeability in a couple of days.
{"title":"Ultrafast Core Analysis for Tight Gas Reservoirs","authors":"N. Bona, D. Santonico, Saida Machicote, A. Battigelli","doi":"10.2118/196648-ms","DOIUrl":"https://doi.org/10.2118/196648-ms","url":null,"abstract":"\u0000 For oil and gas companies, accelerating the time to first hydrocarbon is a strategic objective. Special core analysis programs for tight gas reservoirs may take many months because of the long equilibration times involved in the tests. This represents a bottleneck for achieving the goal of reducing the time-to- market. Both log interpretation and reservoir modelling activities are impacted by the long SCAL durations. In order to face the challenge, a suite of fast methods have been developed. They are fast because they operate under non-equilibrium conditions. The methods give the m&n parameters for electric log interpretation, the endpoint gas relative permeability and the relationship linking initial gas saturation, trapped gas saturation and endpoint water relative permeability in a couple of days.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cristian Masini, Khalid Said Al Shuaili, D. Kuzmichev, Yulia Mironenko, S. Majidaie, R. Buoy, L. Alessio, D. Malakhov, S. Ryzhov, Willem Postuma
Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust. A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks. In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy. The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched. Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming. In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.
{"title":"Locate the Remaining Oil ltro and Predictive Analytics Application for Development Decisions on the Z Field","authors":"Cristian Masini, Khalid Said Al Shuaili, D. Kuzmichev, Yulia Mironenko, S. Majidaie, R. Buoy, L. Alessio, D. Malakhov, S. Ryzhov, Willem Postuma","doi":"10.2118/196631-ms","DOIUrl":"https://doi.org/10.2118/196631-ms","url":null,"abstract":"\u0000 Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust.\u0000 A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks.\u0000 In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy.\u0000 The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched.\u0000 Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming.\u0000 In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126262671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Ghani, S. Ayache, G. Batôt, Julien Gasser-Dorado, E. Delamaide
Although SAGD is a very popular in-situ extraction method in Canada, this thermal process relies on huge energy and water consumption to generate the steam. Irregular growth of the steam-chamber due to heterogeneities further degrades its yield. Contact between the steam chamber and the overburden also leads to heat losses. The objective of this paper is to investigate how Foam Assisted-SAGD could mitigate these technical issues and improve the efficiency of the SAGD process. Compositional thermal reservoir simulations are used to simulate and analyze a Foam Assisted-SAGD pilot. The shear-thinning effect close to the wells is also accounted for. The simulations are run on a homogeneous model mimicking the Foster Creek project in Alberta, Canada. Several type of injection sequences have been analyzed in terms of foam formation, back-produced surfactants and cumulative Steam-Oil-Ratio. Results are compared with the original SAGD performance. In order to propagate the foaming surfactants throughout the steam chamber the injection sequence needs to be properly determined. A simple continuous Foam Assisted-SAGD injection would lead to an accumulation of surfactant between the wells due to gravity segregation, preventing the foam from acting on the upper part of the steam chamber. Furthermore surfactant production occurs after a few weeks due to the proximity of the producer and the injector. A proper injection strategy of the type SAGD/slug/SAGD/slug is found to delay the chemical breakthrough and increase the amount of surfactant retained in the reservoir while allowing the surfactant propagation throughout the steam chamber. After optimization the Foam Assisted-SAGD process appears to be technically promising.
{"title":"Improvement of the SAGD Process by Use of Steam-Foam: Design and Assessment of a Pilot Through Reservoir Simulation","authors":"M. Ghani, S. Ayache, G. Batôt, Julien Gasser-Dorado, E. Delamaide","doi":"10.2118/196676-ms","DOIUrl":"https://doi.org/10.2118/196676-ms","url":null,"abstract":"\u0000 Although SAGD is a very popular in-situ extraction method in Canada, this thermal process relies on huge energy and water consumption to generate the steam. Irregular growth of the steam-chamber due to heterogeneities further degrades its yield. Contact between the steam chamber and the overburden also leads to heat losses. The objective of this paper is to investigate how Foam Assisted-SAGD could mitigate these technical issues and improve the efficiency of the SAGD process. Compositional thermal reservoir simulations are used to simulate and analyze a Foam Assisted-SAGD pilot. The shear-thinning effect close to the wells is also accounted for. The simulations are run on a homogeneous model mimicking the Foster Creek project in Alberta, Canada. Several type of injection sequences have been analyzed in terms of foam formation, back-produced surfactants and cumulative Steam-Oil-Ratio. Results are compared with the original SAGD performance. In order to propagate the foaming surfactants throughout the steam chamber the injection sequence needs to be properly determined. A simple continuous Foam Assisted-SAGD injection would lead to an accumulation of surfactant between the wells due to gravity segregation, preventing the foam from acting on the upper part of the steam chamber. Furthermore surfactant production occurs after a few weeks due to the proximity of the producer and the injector. A proper injection strategy of the type SAGD/slug/SAGD/slug is found to delay the chemical breakthrough and increase the amount of surfactant retained in the reservoir while allowing the surfactant propagation throughout the steam chamber. After optimization the Foam Assisted-SAGD process appears to be technically promising.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122875202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Al-Jenaibi, Konstantin Shelepov, Maksim Kuzevanov, E. Gusarov, K. Bogachev
The application of intelligent algorithms that use clever simplifications and methods to solve computationallycomplex problems are rapidly displacing traditional methods in the petroleum industry. The latest forward-thinking approaches inhistory matching and uncertainty quantification were applied on a dynamic model that has unknown permeability model. The original perm-poro profile was constructed based on synthetic data to compare Assisted History Matching (AHM)approach to the exact solution. It is assumed that relative permeabilities, endpoints, or any parameter other than absolute permeability to match oil/water/gas rates, gas-oil ratio, water injection rate, watercut and bottomhole pressure cannot be modified. The standard approach is to match a model via permeability variation is to split the grid into several regions. However, this process is a complete guess as it is unclear in advance how to select regions. The geological prerequisites for such splitting usually do not exist. Moreover, the values of permeability and porosity in different grid blocks are correlated. Independent change of these values for each region distortscorrelations or make the model unphysical. The proposed alternative involves the decomposition of permeability model into spectrum amplitudes using Discrete Cosine Transformation (DCT), which is a form of Fourier Transform. The sum of all amplitudes in DCT is equal to the original property distribution. Uncertain permeability model typically involves subjective judgment, and several optimization runs to construct uncertainty matrix. However, the proposed multi-objective Particle Swarm Optimization (PSO) helps to reduce randomness and find optimal undominated by any other objective solution with fewer runs. Further optimization of Flexi-PSO algorithm is performed on its constituting components such as swarm size, inertia, nostalgia, sociality, damping factor, neighbor count, neighborliness, the proportion of explorers, egoism, community and relative critical distance to increase the speed of convergence. Additionally, the clustering technique, such as Principal Component Analysis (PCA), is suggested as a mean to reduce the space dimensionality of resulted solutions while ensuring the diversity of selected cluster centers. The presentedset of methodshelps to achieve a qualitative and quantitative match with respect to any property, reduce the number of uncertainty parameters, setup ageneric and efficient approach towards assisted history matching.
{"title":"Analysis of Evolutionary Algorithm and Discrete Cosine Transformation Components Influence on Assisted History Matching Performance","authors":"F. Al-Jenaibi, Konstantin Shelepov, Maksim Kuzevanov, E. Gusarov, K. Bogachev","doi":"10.2118/196686-ms","DOIUrl":"https://doi.org/10.2118/196686-ms","url":null,"abstract":"\u0000 The application of intelligent algorithms that use clever simplifications and methods to solve computationallycomplex problems are rapidly displacing traditional methods in the petroleum industry. The latest forward-thinking approaches inhistory matching and uncertainty quantification were applied on a dynamic model that has unknown permeability model. The original perm-poro profile was constructed based on synthetic data to compare Assisted History Matching (AHM)approach to the exact solution. It is assumed that relative permeabilities, endpoints, or any parameter other than absolute permeability to match oil/water/gas rates, gas-oil ratio, water injection rate, watercut and bottomhole pressure cannot be modified.\u0000 The standard approach is to match a model via permeability variation is to split the grid into several regions. However, this process is a complete guess as it is unclear in advance how to select regions. The geological prerequisites for such splitting usually do not exist. Moreover, the values of permeability and porosity in different grid blocks are correlated. Independent change of these values for each region distortscorrelations or make the model unphysical.\u0000 The proposed alternative involves the decomposition of permeability model into spectrum amplitudes using Discrete Cosine Transformation (DCT), which is a form of Fourier Transform. The sum of all amplitudes in DCT is equal to the original property distribution. Uncertain permeability model typically involves subjective judgment, and several optimization runs to construct uncertainty matrix. However, the proposed multi-objective Particle Swarm Optimization (PSO) helps to reduce randomness and find optimal undominated by any other objective solution with fewer runs. Further optimization of Flexi-PSO algorithm is performed on its constituting components such as swarm size, inertia, nostalgia, sociality, damping factor, neighbor count, neighborliness, the proportion of explorers, egoism, community and relative critical distance to increase the speed of convergence. Additionally, the clustering technique, such as Principal Component Analysis (PCA), is suggested as a mean to reduce the space dimensionality of resulted solutions while ensuring the diversity of selected cluster centers.\u0000 The presentedset of methodshelps to achieve a qualitative and quantitative match with respect to any property, reduce the number of uncertainty parameters, setup ageneric and efficient approach towards assisted history matching.","PeriodicalId":318637,"journal":{"name":"Day 1 Tue, September 17, 2019","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116473355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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