F. Bigoni, M. Pirrone, G. Trombin, Fabio Vinci, Nicola Raimondi Cominesi, A. Guglielmelli, Al Attwi Maher Ali Hassan, Kubbah Salma Ibrahim Uatouf, M. Bazzana, E. Viviani
The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes. In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells. In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%. The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
{"title":"Middle East Karst Carbonate: An Integrated Workflow For Prediction Of Karst Enhancement Distribution","authors":"F. Bigoni, M. Pirrone, G. Trombin, Fabio Vinci, Nicola Raimondi Cominesi, A. Guglielmelli, Al Attwi Maher Ali Hassan, Kubbah Salma Ibrahim Uatouf, M. Bazzana, E. Viviani","doi":"10.2118/196619-ms","DOIUrl":"https://doi.org/10.2118/196619-ms","url":null,"abstract":"\u0000 The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes.\u0000 In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells.\u0000 In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process (\"Supervised Neural Network\") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%.\u0000 The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"8 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113939580","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}
A new real-time machine learning model has been developed based on the deep recurrent neural network (DRNN) model for performing step-down analysis during the hydraulic fracturing process. During a stage of the stimulation process, fluids are inserted at the top of the wellhead, while the flow is primarily driven by the difference between the bottomhole pressure (BHP) and reservoir pressure. The major physics and engineering aspects involved are complex and, quite often, there is a high level of uncertainty related to the accuracy of the measured data, as well as intrinsic noise. Consequently, using a machine learning-based method that can resolve both the temporal and spatial non-linear variations has advantages over a pure engineering model. The approach followed provides a long short-term memory (LSTM) network-based methodology to predict BHP and temperature in a fracturing job, considering all commonly known surface variables. The surface pumping data consists of real-time data captured within each stage, such as surface treating pressure, fluid pumping rate, and proppant rate. The accurate prediction of a response variable, such as BHP, is important because it provides the basis for decisions made in several well treatment applications, such as hydraulic fracturing and matrix acidizing, to ensure success. Limitations of the currently available modeling methods include low resolution BHP predictions and an inability to properly capture non-linear effects in the BHP/temperature time series relationship with other variables, including surface pressure, flow rate, and proppant rate. In addition, current methods are further limited by lack of accuracy in the models for fluid properties; the response of the important sub-surface variables strongly depends on the modeled fluid properties. The novel model presented in this paper uses a deep learning neural network model to predict the BHP and temperature, based on surface pressure, flow rate, and proppant rate. This is the first attempt to predict response variables, such as BHP and temperature, in real time during a pumping stage, using a memory-preserving recurrent neural network (RNN) variant, such as LSTM. The results show that the LSTM can successfully model the BHP and temperature in a hydraulic fracturing process. The BHP and temperature predictions obtained were within 5% relative error. The current effort to model BHP can be used for step-down analysis in real time, thereby providing an accurate representation of the subsurface conditions in the wellbore and in the reservoir. The new method described in this paper avoids the need to manage the complex physics of the present methods; it provides a robust, stable, and accurate numerical solution throughout the pumping stages. The method described in this paper is extended to manage step-down analysis using surface-measured variables to predict perforation and tortuosity friction.
{"title":"Deep Recurrent Neural Network DRNN Model for Real-Time Step-Down Analysis","authors":"S. Madasu, Keshava P. Rangarajan","doi":"10.2118/196621-ms","DOIUrl":"https://doi.org/10.2118/196621-ms","url":null,"abstract":"\u0000 A new real-time machine learning model has been developed based on the deep recurrent neural network (DRNN) model for performing step-down analysis during the hydraulic fracturing process. During a stage of the stimulation process, fluids are inserted at the top of the wellhead, while the flow is primarily driven by the difference between the bottomhole pressure (BHP) and reservoir pressure. The major physics and engineering aspects involved are complex and, quite often, there is a high level of uncertainty related to the accuracy of the measured data, as well as intrinsic noise. Consequently, using a machine learning-based method that can resolve both the temporal and spatial non-linear variations has advantages over a pure engineering model.\u0000 The approach followed provides a long short-term memory (LSTM) network-based methodology to predict BHP and temperature in a fracturing job, considering all commonly known surface variables. The surface pumping data consists of real-time data captured within each stage, such as surface treating pressure, fluid pumping rate, and proppant rate. The accurate prediction of a response variable, such as BHP, is important because it provides the basis for decisions made in several well treatment applications, such as hydraulic fracturing and matrix acidizing, to ensure success.\u0000 Limitations of the currently available modeling methods include low resolution BHP predictions and an inability to properly capture non-linear effects in the BHP/temperature time series relationship with other variables, including surface pressure, flow rate, and proppant rate. In addition, current methods are further limited by lack of accuracy in the models for fluid properties; the response of the important sub-surface variables strongly depends on the modeled fluid properties.\u0000 The novel model presented in this paper uses a deep learning neural network model to predict the BHP and temperature, based on surface pressure, flow rate, and proppant rate. This is the first attempt to predict response variables, such as BHP and temperature, in real time during a pumping stage, using a memory-preserving recurrent neural network (RNN) variant, such as LSTM. The results show that the LSTM can successfully model the BHP and temperature in a hydraulic fracturing process. The BHP and temperature predictions obtained were within 5% relative error. The current effort to model BHP can be used for step-down analysis in real time, thereby providing an accurate representation of the subsurface conditions in the wellbore and in the reservoir. The new method described in this paper avoids the need to manage the complex physics of the present methods; it provides a robust, stable, and accurate numerical solution throughout the pumping stages. The method described in this paper is extended to manage step-down analysis using surface-measured variables to predict perforation and tortuosity friction.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"3 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":"128758052","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}
Waterflooding is the main technic to recover hydrocarbons in reservoirs. For a given set of wells (injectors and producers), the choice of injection/production parameters such as pressures, flow rates, and locations of these boundary conditions have a significant impact on the operating life of the wells. As a large number of combinations of these parameters are possible, one of the critical decision to make is to identify an optimal set of these parameters. Using the reservoir simulator directly to evaluate the impact of these sets being unrealistic considering the required number of simulations, a common approach consists of using response surfaces to approximate the reservoir simulator outputs. Several techniques involving proxies model (e.g., kriging, polynomial, and artificial neural network) have been suggested to replace the reservoir simulations. This paper focalizes on the application of artificial neural networks (ANN) as it is commonly admitted that the ANNs are the most efficient one due to their universal approximation capacity, i.e., capacity to reproduce any continuous function. This paper presents a complete workflow to optimize well parameters under waterflooding using an artificial neural network as a proxy model. The proposed methodology allows evaluating different production configurations that maximize the NPV according to a given risk. The optimized solutions can be analyzed with the efficient frontier plot and the Sharpe ratios. An application of the workflow to the Brugge field is presented in order to optimize the waterflooding strategy.
{"title":"Optimization of Waterflooding Strategy Using Artificial Neural Networks","authors":"J. Bruyelle, D. Guérillot","doi":"10.2118/196643-ms","DOIUrl":"https://doi.org/10.2118/196643-ms","url":null,"abstract":"\u0000 Waterflooding is the main technic to recover hydrocarbons in reservoirs. For a given set of wells (injectors and producers), the choice of injection/production parameters such as pressures, flow rates, and locations of these boundary conditions have a significant impact on the operating life of the wells. As a large number of combinations of these parameters are possible, one of the critical decision to make is to identify an optimal set of these parameters. Using the reservoir simulator directly to evaluate the impact of these sets being unrealistic considering the required number of simulations, a common approach consists of using response surfaces to approximate the reservoir simulator outputs. Several techniques involving proxies model (e.g., kriging, polynomial, and artificial neural network) have been suggested to replace the reservoir simulations. This paper focalizes on the application of artificial neural networks (ANN) as it is commonly admitted that the ANNs are the most efficient one due to their universal approximation capacity, i.e., capacity to reproduce any continuous function. This paper presents a complete workflow to optimize well parameters under waterflooding using an artificial neural network as a proxy model. The proposed methodology allows evaluating different production configurations that maximize the NPV according to a given risk. The optimized solutions can be analyzed with the efficient frontier plot and the Sharpe ratios. An application of the workflow to the Brugge field is presented in order to optimize the waterflooding strategy.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"44 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":"134205335","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}
Multi-rock type cores can be characterized by complex higher order connectivity relationships within an agglomerated petrophysical system. A solution that relates multiphase flow simulation in cores to time-lapse seismic properties in order to examine closed-loop 4D integration is performed at a high level on a plug. While a 4D workflow is not explicitly examined in this work, the requisite petro-elastic modeling (PEM) method based on a simulation-driven interpretation of the Gassmann equation is described and a comparison is made with its empirically derived counterpart. This work illustrates that a simulation-driven petro-elastic modeling approach can be used to generate time-dependent saturated rock properties consistent with seismic attribute description at the plug and core scales. The results demonstrate the simulation-driven approach, of a petro-elastic model embedded in a reservoir simulator, as an alternative to relating pressure and saturation from reservoir simulator-to-seismic-derived properties using a priori empirically based correlations. The method discussed in this paper maintains appreciable continuity with the results of empirically based petro-elastic methods but demonstrates differences commensurate with principal fluid differentiation capability inherent to reservoir simulator-derived data and observed time-lapse seismic response. The significance of applied multi-porosity relationships is further realized upon examination of the time-dependent petro-elastic model results.
{"title":"Petro-Elastic Modeling Applied to Multi-Porosity/Multi-Permeability Cores through a Simulation to Seismic Method","authors":"T. Ramsay, Aravind Prabhakar","doi":"10.2118/196685-ms","DOIUrl":"https://doi.org/10.2118/196685-ms","url":null,"abstract":"\u0000 Multi-rock type cores can be characterized by complex higher order connectivity relationships within an agglomerated petrophysical system. A solution that relates multiphase flow simulation in cores to time-lapse seismic properties in order to examine closed-loop 4D integration is performed at a high level on a plug. While a 4D workflow is not explicitly examined in this work, the requisite petro-elastic modeling (PEM) method based on a simulation-driven interpretation of the Gassmann equation is described and a comparison is made with its empirically derived counterpart. This work illustrates that a simulation-driven petro-elastic modeling approach can be used to generate time-dependent saturated rock properties consistent with seismic attribute description at the plug and core scales. The results demonstrate the simulation-driven approach, of a petro-elastic model embedded in a reservoir simulator, as an alternative to relating pressure and saturation from reservoir simulator-to-seismic-derived properties using a priori empirically based correlations. The method discussed in this paper maintains appreciable continuity with the results of empirically based petro-elastic methods but demonstrates differences commensurate with principal fluid differentiation capability inherent to reservoir simulator-derived data and observed time-lapse seismic response. The significance of applied multi-porosity relationships is further realized upon examination of the time-dependent petro-elastic model results.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"50 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":"116200807","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}
Mohamed AlBreiki, S. AlAmeri, S. Geiger, P. Corbett
An innovative multi-deterministic scenario workflow was applied to one of the giant and complex carbonate reservoirs in the Middle East. The application of this workflow had the objective to quantify how geological uncertainties and different modelling decisions impact the stock tank oil-initially-in-place (STOIIP) estimates and flow behaviour in this reservoir. In particular, we focused on the uncertainties related to the presence of fractures, reservoir rock typing, and modelling the initial hydrocarbon distribution. Based on the available static and dynamic data we considered two key scenarios, the absence of fractures and the presence of sparse, fault-controlled fractures. In the first scenario, we investigated how different reservoir rock typing methods impact permeability distributions. We further quantified changes in hydrocarbon distribution and analysed how a novel approach that combines capillary pressure and log-derived J-function affects the saturation models. In the second scenario, we used the effective medium theory to calculate permeability multipliers for the regions where fractures are expected. This enabled us to effectively represent fractures in a single-porosity reservoir model. The representativeness of the different models was analysed through blind tests using static data as well as history matching using dynamic data. The most significant findings of our work are that subtle changes in modelling decisions and reservoir rock typing have major consequences for the saturation model, leading to up to 20% change in STOIIP estimates. Such uncertainties must be carried forward in future reservoir management decisions and when estimating reserves. The blind tests showed that a saturation model based on the combination of core- and log-derived J-functions gave the most robust STOIIP estimates. These particular saturation models further led to a much-improved history match, especially for wells located in the transition zone of the reservoir. The best history matches were obtained once sparse, fault-controlled fractures were included in the reservoir model using effective medium theory. The presence of fractures specifically improved the history matching quality for wells located close to the faults; these wells were very difficult to match in the past. Our work clearly demonstrates that a multi-deterministic scenario workflow is key to explore the appropriate range of geological uncertainties, and that, equally important, the impact of different modelling decisions must be accounted for when quantifying uncertainty during reservoir modelling. This is particularly applicable to giant carbonate reservoirs where relatively minor changes in the workflow and data interpretation can have major consequences on STOIIP estimates, dynamic behaviours, and reserve estimates. Multi-stochastic modelling workflows which anchor the reservoir to a single base case are not capable of achieving this.
{"title":"An Integrated Workflow for Quantifying the Impact of Geological Uncertainty and Modelling Decisions On STOIIP Estimates and History Matching - A Case Study from the Middle East","authors":"Mohamed AlBreiki, S. AlAmeri, S. Geiger, P. Corbett","doi":"10.2118/196679-ms","DOIUrl":"https://doi.org/10.2118/196679-ms","url":null,"abstract":"\u0000 An innovative multi-deterministic scenario workflow was applied to one of the giant and complex carbonate reservoirs in the Middle East. The application of this workflow had the objective to quantify how geological uncertainties and different modelling decisions impact the stock tank oil-initially-in-place (STOIIP) estimates and flow behaviour in this reservoir. In particular, we focused on the uncertainties related to the presence of fractures, reservoir rock typing, and modelling the initial hydrocarbon distribution.\u0000 Based on the available static and dynamic data we considered two key scenarios, the absence of fractures and the presence of sparse, fault-controlled fractures. In the first scenario, we investigated how different reservoir rock typing methods impact permeability distributions. We further quantified changes in hydrocarbon distribution and analysed how a novel approach that combines capillary pressure and log-derived J-function affects the saturation models. In the second scenario, we used the effective medium theory to calculate permeability multipliers for the regions where fractures are expected. This enabled us to effectively represent fractures in a single-porosity reservoir model. The representativeness of the different models was analysed through blind tests using static data as well as history matching using dynamic data.\u0000 The most significant findings of our work are that subtle changes in modelling decisions and reservoir rock typing have major consequences for the saturation model, leading to up to 20% change in STOIIP estimates. Such uncertainties must be carried forward in future reservoir management decisions and when estimating reserves. The blind tests showed that a saturation model based on the combination of core- and log-derived J-functions gave the most robust STOIIP estimates. These particular saturation models further led to a much-improved history match, especially for wells located in the transition zone of the reservoir. The best history matches were obtained once sparse, fault-controlled fractures were included in the reservoir model using effective medium theory. The presence of fractures specifically improved the history matching quality for wells located close to the faults; these wells were very difficult to match in the past.\u0000 Our work clearly demonstrates that a multi-deterministic scenario workflow is key to explore the appropriate range of geological uncertainties, and that, equally important, the impact of different modelling decisions must be accounted for when quantifying uncertainty during reservoir modelling. This is particularly applicable to giant carbonate reservoirs where relatively minor changes in the workflow and data interpretation can have major consequences on STOIIP estimates, dynamic behaviours, and reserve estimates. Multi-stochastic modelling workflows which anchor the reservoir to a single base case are not capable of achieving this.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"53 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":"125967058","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}
A. Serry, S. Yousif, A. Abouzaid, Piyanuch Kieduppatum, A. Soliman
Full field development of the Upper Jurassic carbonates, offshore Abu Dhabi is exceedingly challenging. The heterogeneous texture, complicated pore systems and intensive lithology changes all mark the regressive cycles of sedimentation. Such complicated characteristics obscure formation evaluation of these formations. Advanced well logging tools and interpretation methodologies are implemented to minimize the petrophysical uncertainties to qualify the products as field development critical elements. This case study highlights a newly applied NMR log interpretation approach. The results help to understand the complex pore system in a tight carbonate layer, along a horizontal drain drilled close to the oil-water contact. NMR log data was acquired in real-time while drilling simultaneously with Gamma Ray, Resistivity and Image Logs. Earlier field studies recommended swapping standard T2 free fluid relaxation cutoff values by actual laboratory NMR measurements for a higher precision suitable for the reservoir texture heterogeneity, the study itself supported the application of higher cutoff values to better discriminate the free fluid in well-connected macro pores from the irreducible which will have a direct impact on the computed permeability. In this case study, a variable free-fluid T2 cutoff was firstly implemented based on arbitrary estimations to match the computed Coates permeability to the offset core values. Free-fluid, irreducible fluids were sequentially computed. A unique NMR-Gamma Inversion (NMR-GI) workflow is further utilized as a mathematically defined approach to process the raw data using probabilistic functions. The result is a more precise pore size distribution, coherent with the geological variations. NMR Capillary pressure was computed. The complex formation texture could be accurately tracked for thousands of feet drilled along the horizontal drain. After validation with offset core, the NMR-GI interpretation was combined with, Archie saturation and Image log analysis for a conclusive assessment. Hydraulic flow units were combined. Successful completion design and production zone selection articulated on the defined open hole log interpretation. NMR while drilling logging and the applied (NMR-GI) methodology prove to be leading tools to assist in resolving carbonate reservoir complexities. Not only that they help to understand the pore system characteristics, but they effectively support well placement, completion and production.
{"title":"An Enhanced NMR Workflow for Heterogeneous Carbonates Characterization, Offshore Abu Dhabi","authors":"A. Serry, S. Yousif, A. Abouzaid, Piyanuch Kieduppatum, A. Soliman","doi":"10.2118/196710-ms","DOIUrl":"https://doi.org/10.2118/196710-ms","url":null,"abstract":"\u0000 Full field development of the Upper Jurassic carbonates, offshore Abu Dhabi is exceedingly challenging. The heterogeneous texture, complicated pore systems and intensive lithology changes all mark the regressive cycles of sedimentation. Such complicated characteristics obscure formation evaluation of these formations. Advanced well logging tools and interpretation methodologies are implemented to minimize the petrophysical uncertainties to qualify the products as field development critical elements. This case study highlights a newly applied NMR log interpretation approach. The results help to understand the complex pore system in a tight carbonate layer, along a horizontal drain drilled close to the oil-water contact.\u0000 NMR log data was acquired in real-time while drilling simultaneously with Gamma Ray, Resistivity and Image Logs. Earlier field studies recommended swapping standard T2 free fluid relaxation cutoff values by actual laboratory NMR measurements for a higher precision suitable for the reservoir texture heterogeneity, the study itself supported the application of higher cutoff values to better discriminate the free fluid in well-connected macro pores from the irreducible which will have a direct impact on the computed permeability.\u0000 In this case study, a variable free-fluid T2 cutoff was firstly implemented based on arbitrary estimations to match the computed Coates permeability to the offset core values. Free-fluid, irreducible fluids were sequentially computed. A unique NMR-Gamma Inversion (NMR-GI) workflow is further utilized as a mathematically defined approach to process the raw data using probabilistic functions. The result is a more precise pore size distribution, coherent with the geological variations. NMR Capillary pressure was computed.\u0000 The complex formation texture could be accurately tracked for thousands of feet drilled along the horizontal drain. After validation with offset core, the NMR-GI interpretation was combined with, Archie saturation and Image log analysis for a conclusive assessment. Hydraulic flow units were combined. Successful completion design and production zone selection articulated on the defined open hole log interpretation.\u0000 NMR while drilling logging and the applied (NMR-GI) methodology prove to be leading tools to assist in resolving carbonate reservoir complexities. Not only that they help to understand the pore system characteristics, but they effectively support well placement, completion and production.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"6 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":"123995137","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}
The traditional definition of volumetric sweep efficiency sums the effects of both fingering (arising due to contrasts in mobility) and bypassing (arising due to contrasts in permeability as well as well placement). Accordingly, we cannot quantitatively attribute poor sweep to either bypassing or fingering. Similarly, in EOR, the incremental recovery cannot be quantitatively associated with the reduction of those effects. For such purposes, we rely on visualization and mapping of saturation profiles to quantify and characterize the remaining oil in place including its distribution. . In this work, we propose a complementary method to obtain an instantaneous insight of the remaining oil distribution. We demonstrate the decomposition of fingering and bypassing effects and its utility. We first redefine recovery factors such that we decouple bypassing and fingering effects. We then validate those redefined sweep indicators by examining a 5-spot waterflood and two idealistic polymer floods. Later, we demonstrate the possible utility of those redefined sweep indicators through different examples. In one example, we compare the performance of a shear - thinning polymer to a recovery-equivalent Newtonian polymer. Using fingering and bypassing sweep indicators, we can immediately conclude that the shear-thinning polymer exacerbates bypassing. We recommend the adoption of our redefined sweep indicators in any simulation suite. They provide instant understanding of sweep and hence can be complementary to standard practices of oil saturation mapping and of special value when analyzing the results of multiple realizations and/or development scenarios.
{"title":"The Decomposition of Volumetric Sweep Efficiency and Its Utility","authors":"A. AlSofi, M. Blunt","doi":"10.2118/196632-ms","DOIUrl":"https://doi.org/10.2118/196632-ms","url":null,"abstract":"\u0000 The traditional definition of volumetric sweep efficiency sums the effects of both fingering (arising due to contrasts in mobility) and bypassing (arising due to contrasts in permeability as well as well placement). Accordingly, we cannot quantitatively attribute poor sweep to either bypassing or fingering. Similarly, in EOR, the incremental recovery cannot be quantitatively associated with the reduction of those effects. For such purposes, we rely on visualization and mapping of saturation profiles to quantify and characterize the remaining oil in place including its distribution. . In this work, we propose a complementary method to obtain an instantaneous insight of the remaining oil distribution. We demonstrate the decomposition of fingering and bypassing effects and its utility. We first redefine recovery factors such that we decouple bypassing and fingering effects. We then validate those redefined sweep indicators by examining a 5-spot waterflood and two idealistic polymer floods. Later, we demonstrate the possible utility of those redefined sweep indicators through different examples. In one example, we compare the performance of a shear - thinning polymer to a recovery-equivalent Newtonian polymer. Using fingering and bypassing sweep indicators, we can immediately conclude that the shear-thinning polymer exacerbates bypassing. We recommend the adoption of our redefined sweep indicators in any simulation suite. They provide instant understanding of sweep and hence can be complementary to standard practices of oil saturation mapping and of special value when analyzing the results of multiple realizations and/or development scenarios.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"89 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":"129428647","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}
The high CO2 content of Brazil’s pre-salt fields, which may reach values from 20% to 44% molar, presents both a challenge as well as an opportunity. CO2 stripped from the produced gas cannot be released into the atmosphere due to environmental restrictions. Therefore, the whole amount of CO2 produced should be continuously reinjected into the reservoir. This work investigates the effect of CO2 content on the low salinity water alternating CO2 injection technique (CO2LSWAG) using a commercial compositional reservoir simulator. In these field-scale simulations, CO2 is stripped from the produced gas and reinjected into the reservoir. Primary oil recovery methods such as CO2 flooding and LSW flooding are also simulated. Chemical reactions between CO2 and the minerals present in the reservoir are modeled. Wettability change is assumed to be the main mechanism for improved oil recovery due to low salinity water injection. Compositional simulations of CO2 injection usually assume a constant injected gas rate. In this case, CO2 is supposed to come from an external source. In many petroleum reservoirs this assumption is true. Three factors are assessed in the present work. The first one is the natural reservoir pressure, which is the main driving force in primary production. The second factor is the amount of CO2 available for injection. The third one is the wettability change promoted by the reaction involving CO2. It is shown that in primary production, higher CO2 content leads to quicker depletion of the natural energy of the reservoir, leading to lower oil recovery. Nevertheless, higher CO2 content also means that more gas is available for reinjection, potentially leading to increased oil production. Finally, as CO2 reacts with minerals it promotes a change in wettability from an oil-wet to a water-wet state. It is shown that the CO2 content is an important variable to be assessed in a high CO2 content reservoir. Optimal injection practices must take these three aspects into consideration.
{"title":"Simulation of Enhanced Oil Recovery in Pre-Salt Reservoirs: The Effect of High CO2 Content on Low Salinity Water Alternating Gas Injection","authors":"A. S. Carvalhal, G. Costa, S. V. D. Melo","doi":"10.2118/196684-ms","DOIUrl":"https://doi.org/10.2118/196684-ms","url":null,"abstract":"\u0000 The high CO2 content of Brazil’s pre-salt fields, which may reach values from 20% to 44% molar, presents both a challenge as well as an opportunity. CO2 stripped from the produced gas cannot be released into the atmosphere due to environmental restrictions. Therefore, the whole amount of CO2 produced should be continuously reinjected into the reservoir. This work investigates the effect of CO2 content on the low salinity water alternating CO2 injection technique (CO2LSWAG) using a commercial compositional reservoir simulator. In these field-scale simulations, CO2 is stripped from the produced gas and reinjected into the reservoir. Primary oil recovery methods such as CO2 flooding and LSW flooding are also simulated. Chemical reactions between CO2 and the minerals present in the reservoir are modeled. Wettability change is assumed to be the main mechanism for improved oil recovery due to low salinity water injection. Compositional simulations of CO2 injection usually assume a constant injected gas rate. In this case, CO2 is supposed to come from an external source. In many petroleum reservoirs this assumption is true. Three factors are assessed in the present work. The first one is the natural reservoir pressure, which is the main driving force in primary production. The second factor is the amount of CO2 available for injection. The third one is the wettability change promoted by the reaction involving CO2. It is shown that in primary production, higher CO2 content leads to quicker depletion of the natural energy of the reservoir, leading to lower oil recovery. Nevertheless, higher CO2 content also means that more gas is available for reinjection, potentially leading to increased oil production. Finally, as CO2 reacts with minerals it promotes a change in wettability from an oil-wet to a water-wet state. It is shown that the CO2 content is an important variable to be assessed in a high CO2 content reservoir. Optimal injection practices must take these three aspects into consideration.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 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":"129574981","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}
Chenji Wei, Jie Zheng, Xiaohu Ouyang, Yutao Ding, Mingming Ding, Shiyao Lin, Hongqing Song
Understanding the heterogeneity is critical for a successful water injection in a carbonate reservoir. Thief zone is one of the most obvious forms of heterogeneity, which indicates the thin layer with higher permeability compared to the average reservoir permeability. The existence of thief zone results in earlier water breakthrough and faster water cut increase, which then lead to lower sweep efficiency and smaller recovery factor. Therefore, determining the distribution of thief zone and its impact towards production, and proposing a corresponding development plan are very important. In this paper, a novel method is established to determine the thief zone distribution based on dynamic surveillance data. A new index is proposed as the relative contribution index to characterize the relative contribution of a certain layer, which is fundamental for thief zone determination. In addition, effect on water flooding development of thief zone's location is studied by experimental and theoretical analysis. The changes of water cut and production rate are analyzed under different conditions such as location of the thief zone, injection rate, and variogram. Finally, optimized development strategy is proposed to deal with the existence of thief zone. Distribution of thief zone is characterized based on the proposed method, which indicates that thief zone development has intimate relationship with depositional facies and diagenesis. Experimental and theoretical analysis results show that the present model considering stratified water-flood is consistent with the experimental results. The water displacement effect is the best when the thief zone is located in the upper reservoir. This paper also points out the optimal adjustment period for water shutoff and profile control of the reservoir with thief zones. In addition, the greater the injection rate, the faster the water cut increase. Furthermore, the smaller the variogram, the slower the water cut increase, and the later the water breakthrough time. This study provides a method to characterize thief zone, which can be used as a reference for similar oilfield development. In addition, it provides a quick and reasonable guide in the later adjustment of water flooding development of carbonate reservoirs with thief zones.
{"title":"Thief Zone Characterization and its Impact on Well Performance Based on Surveillance Data, Experimental Data and Theoretical Analysis for a Carbonate Reservoir","authors":"Chenji Wei, Jie Zheng, Xiaohu Ouyang, Yutao Ding, Mingming Ding, Shiyao Lin, Hongqing Song","doi":"10.2118/196627-ms","DOIUrl":"https://doi.org/10.2118/196627-ms","url":null,"abstract":"\u0000 Understanding the heterogeneity is critical for a successful water injection in a carbonate reservoir. Thief zone is one of the most obvious forms of heterogeneity, which indicates the thin layer with higher permeability compared to the average reservoir permeability. The existence of thief zone results in earlier water breakthrough and faster water cut increase, which then lead to lower sweep efficiency and smaller recovery factor. Therefore, determining the distribution of thief zone and its impact towards production, and proposing a corresponding development plan are very important.\u0000 In this paper, a novel method is established to determine the thief zone distribution based on dynamic surveillance data. A new index is proposed as the relative contribution index to characterize the relative contribution of a certain layer, which is fundamental for thief zone determination. In addition, effect on water flooding development of thief zone's location is studied by experimental and theoretical analysis. The changes of water cut and production rate are analyzed under different conditions such as location of the thief zone, injection rate, and variogram. Finally, optimized development strategy is proposed to deal with the existence of thief zone.\u0000 Distribution of thief zone is characterized based on the proposed method, which indicates that thief zone development has intimate relationship with depositional facies and diagenesis. Experimental and theoretical analysis results show that the present model considering stratified water-flood is consistent with the experimental results. The water displacement effect is the best when the thief zone is located in the upper reservoir. This paper also points out the optimal adjustment period for water shutoff and profile control of the reservoir with thief zones. In addition, the greater the injection rate, the faster the water cut increase. Furthermore, the smaller the variogram, the slower the water cut increase, and the later the water breakthrough time.\u0000 This study provides a method to characterize thief zone, which can be used as a reference for similar oilfield development. In addition, it provides a quick and reasonable guide in the later adjustment of water flooding development of carbonate reservoirs with thief zones.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 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":"125031618","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}
Development of oil rim reservoirs is challenging and could lead to low oil recovery, if multiple determining factors are not well understood, that influences successful field development concept. It requires detailed analysis and development of specific procedures to optimize the oil production from a thin oil rim underlaying gas cap. Few IOR/EOR applications for oil rim development have been reported in the literature so far. This study presents a concept for the optimization of oil production from an oil rim reservoir by numerical simulation. As a starting point, a representative sector of the field was selected for the initial analysis. It was decided to perform IOR/EOR methods including water/gas flooding/injection and surfactant flooding using inverted five-spot horizontal well pattern, for the application in the selected sector. Upon execution of the detailed sensitivity analysis, the pattern was optimized by its characteristic geometric variables including the length of the vertical/horizontal section of the well, the location of the wells, lateral well distances and the orientation of the pattern. The optimization was performed by setting an objective function to improve recovery factor and reduce water/gas cut by using the differential evolution algorithm. The latter was run until converging, and the optimal solution was used to perform further IOR/EOR studies. Finally, after selection of a base-case scenario and best well pattern, IOR/EOR options were evaluated, and the comparative results were reported. The generated results show that the application of 5-spot horizontal well pattern in the oil rim reservoir could increase the oil recovery by water flooding, but with low sweep efficiency. The losses of injected water into the underlaying aquifer and up laying gas gap are large. Immiscible gas injection into the gas cap can support the pressure but massively increases the gas cut. In addition, displacement efficiency by gas flooding is poor. Simulation results of the surfactant flooding case shows better displacement efficiency compared to water flooding. Also, the possibility of reducing residual oil saturation could increase the ultimate oil recovery but at very late time.
{"title":"Optimization of Oil Production in an Oil Rim Reservoir Using Numerical Simulation with Focus on IOR/EOR Application","authors":"Maroua Jaoua, M. Rafiee","doi":"10.2118/196709-ms","DOIUrl":"https://doi.org/10.2118/196709-ms","url":null,"abstract":"\u0000 Development of oil rim reservoirs is challenging and could lead to low oil recovery, if multiple determining factors are not well understood, that influences successful field development concept. It requires detailed analysis and development of specific procedures to optimize the oil production from a thin oil rim underlaying gas cap. Few IOR/EOR applications for oil rim development have been reported in the literature so far. This study presents a concept for the optimization of oil production from an oil rim reservoir by numerical simulation.\u0000 As a starting point, a representative sector of the field was selected for the initial analysis. It was decided to perform IOR/EOR methods including water/gas flooding/injection and surfactant flooding using inverted five-spot horizontal well pattern, for the application in the selected sector. Upon execution of the detailed sensitivity analysis, the pattern was optimized by its characteristic geometric variables including the length of the vertical/horizontal section of the well, the location of the wells, lateral well distances and the orientation of the pattern. The optimization was performed by setting an objective function to improve recovery factor and reduce water/gas cut by using the differential evolution algorithm. The latter was run until converging, and the optimal solution was used to perform further IOR/EOR studies.\u0000 Finally, after selection of a base-case scenario and best well pattern, IOR/EOR options were evaluated, and the comparative results were reported. The generated results show that the application of 5-spot horizontal well pattern in the oil rim reservoir could increase the oil recovery by water flooding, but with low sweep efficiency. The losses of injected water into the underlaying aquifer and up laying gas gap are large. Immiscible gas injection into the gas cap can support the pressure but massively increases the gas cut. In addition, displacement efficiency by gas flooding is poor.\u0000 Simulation results of the surfactant flooding case shows better displacement efficiency compared to water flooding. Also, the possibility of reducing residual oil saturation could increase the ultimate oil recovery but at very late time.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"3 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":"128115510","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}