Mohammed Al Hamad, Ping Zhang, Ahmad M. AlZoukani, B. Altundas, Wael Abdallah
Dynamic water, also known as smart water, injected at the end of conventional water flood by seawater, is known to show significant improvement in recovering additional oil. Different mechanisms have been proposed and lab measurements were conducted to understand the underlying process of additional oil recovery through dynamic water injection in lab conditions. In this work, we study the effects of different dynamic water injection scenarios on oil recovery in carbonate reservoirs based on reservoir simulations using representative fluid and rock properties with relative permeability curves obtained from core studies. To quantify the changes in measurable multiphysics properties due to dynamic water injection and reconcile multiphysics interpretation with additional oil recovery at field scale, a petrophysically consistent multiphysics effective property modeling is conducted. Based on the simulation results, dynamic water injection is shown to be effective in additional oil recovery at field scale post seawater injection. In addition, saturation changes caused by dynamic water injection result in detectable time-lapse contrast in the corresponding conductivity profiles, suggesting feasibility of the resistivity measurements to monitor dynamic water injection. This paper shows the advantages and benefits of petrophysically consistent multiphysics effective property modeling for a successful fluid monitoring design for quantifying the efficiency of dynamic water injection on additional oil recovery post seawater flood.
{"title":"Monitoring Dynamic Water Injection to Improve Oil Recovery Efficiency","authors":"Mohammed Al Hamad, Ping Zhang, Ahmad M. AlZoukani, B. Altundas, Wael Abdallah","doi":"10.2118/204755-ms","DOIUrl":"https://doi.org/10.2118/204755-ms","url":null,"abstract":"\u0000 Dynamic water, also known as smart water, injected at the end of conventional water flood by seawater, is known to show significant improvement in recovering additional oil. Different mechanisms have been proposed and lab measurements were conducted to understand the underlying process of additional oil recovery through dynamic water injection in lab conditions. In this work, we study the effects of different dynamic water injection scenarios on oil recovery in carbonate reservoirs based on reservoir simulations using representative fluid and rock properties with relative permeability curves obtained from core studies. To quantify the changes in measurable multiphysics properties due to dynamic water injection and reconcile multiphysics interpretation with additional oil recovery at field scale, a petrophysically consistent multiphysics effective property modeling is conducted. Based on the simulation results, dynamic water injection is shown to be effective in additional oil recovery at field scale post seawater injection. In addition, saturation changes caused by dynamic water injection result in detectable time-lapse contrast in the corresponding conductivity profiles, suggesting feasibility of the resistivity measurements to monitor dynamic water injection. This paper shows the advantages and benefits of petrophysically consistent multiphysics effective property modeling for a successful fluid monitoring design for quantifying the efficiency of dynamic water injection on additional oil recovery post seawater flood.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79545567","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 paper describes a new electrical submersible pump (ESP) design concept to overcome the challenges of applications in slim well completions or thru-tubing deployment. The housing of the conventional pump is removed, allowing the pump impellers to have a larger diameter. The impact of this design change on pump hydraulic performance is assessed in this paper. Downhole ESPs operate in environments where space is limited radially. This is especially the case for slim completions or for thru-tubing rigless deployment. To provide the required rate and total dynamic head, the current approach is to use permanent magnetic motors and operate the slim systems at rotational speed over the conventional speed of 3500-4000 RPM. High-speed operations require new pump stage designs to minimize erosion and vibration. This paper provides an alternative pump design, which removes the pump housing with the benefit of increasing the impeller tip diameter, and hence potentially reducing pump length and operational speed. To ensure the pump retains the well fluids, the diffusers are designed to be externally threaded with an O-ring feature. The centrifugal pump affinity laws are applied to evaluate the impact of removing the pump housing and increasing the impeller outside diameter. A typical ESP housing wall thickness is about 0.18-0.25 inch. With the housing removed, the incremental space available for the impeller tip to occupy is increased by 0.36-0.5 inch. Analysis shows that, for the same pump speed as a conventional pump with a housing, a housingless pump will increase the head generated by 23-32%, and the rate capacity about 36-51%, depending on the pump series. In general, the smaller the pump outer diameter, the greater the flow and head capacity increase. This is because the available space due to removing the housing becomes a considerable size of the impeller tip diameter for the smaller series pumps. The elimination of pump housing enables impellers with a larger diameter to be used to generate more head per stage. In comparison to a conventional pump of the same outside diameter, and providing the same amount of total dynamic head, the housingless pump can have fewer stages and a shorter length or operate at a reduced speed. The reduced length can help mitigating pump-bending stress for installation in deviated or horizontal wells. The reduction in required operating speeds will reduce pump wears, heat generation and vibration. The housingless ESPs have applications for slim well completions or thru-tubing deployments.
{"title":"Housingless ESPs for Slim Completion Wells","authors":"Jinjiang Xiao, C. Ejim","doi":"10.2118/204750-ms","DOIUrl":"https://doi.org/10.2118/204750-ms","url":null,"abstract":"\u0000 This paper describes a new electrical submersible pump (ESP) design concept to overcome the challenges of applications in slim well completions or thru-tubing deployment. The housing of the conventional pump is removed, allowing the pump impellers to have a larger diameter. The impact of this design change on pump hydraulic performance is assessed in this paper.\u0000 Downhole ESPs operate in environments where space is limited radially. This is especially the case for slim completions or for thru-tubing rigless deployment. To provide the required rate and total dynamic head, the current approach is to use permanent magnetic motors and operate the slim systems at rotational speed over the conventional speed of 3500-4000 RPM. High-speed operations require new pump stage designs to minimize erosion and vibration. This paper provides an alternative pump design, which removes the pump housing with the benefit of increasing the impeller tip diameter, and hence potentially reducing pump length and operational speed. To ensure the pump retains the well fluids, the diffusers are designed to be externally threaded with an O-ring feature. The centrifugal pump affinity laws are applied to evaluate the impact of removing the pump housing and increasing the impeller outside diameter.\u0000 A typical ESP housing wall thickness is about 0.18-0.25 inch. With the housing removed, the incremental space available for the impeller tip to occupy is increased by 0.36-0.5 inch. Analysis shows that, for the same pump speed as a conventional pump with a housing, a housingless pump will increase the head generated by 23-32%, and the rate capacity about 36-51%, depending on the pump series. In general, the smaller the pump outer diameter, the greater the flow and head capacity increase. This is because the available space due to removing the housing becomes a considerable size of the impeller tip diameter for the smaller series pumps.\u0000 The elimination of pump housing enables impellers with a larger diameter to be used to generate more head per stage. In comparison to a conventional pump of the same outside diameter, and providing the same amount of total dynamic head, the housingless pump can have fewer stages and a shorter length or operate at a reduced speed. The reduced length can help mitigating pump-bending stress for installation in deviated or horizontal wells. The reduction in required operating speeds will reduce pump wears, heat generation and vibration. The housingless ESPs have applications for slim well completions or thru-tubing deployments.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75759619","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}
Mariam Shreif, S. Kalam, Mohammad Rasheed Khan, Rizwan Ahmed Khan
During the past decades, several research studies have been made to unfold the immense and diversified benefits of the innovative applications of machine learning (ML) techniques in the petroleum industry. For instance, machine learning algorithms were applied to estimate the various physical properties of natural gas. Natural gas density is considered an indispensable metric that influences the determination of several variables necessary for analyzing natural gas systems. In this work, the Artificial neural network (ANN), a machine learning technique, was applied to estimate natural gas density incorporating the influencing factors. The ANN model was also compared with another ML technique, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS). A mathematical form has been also presented using ANN. A real data set was taken from the literature, comprised of about 4500 data points assimilating three influencing input variables, including pseudo-reduced pressure (PPr), pseudo-reduced temperature (TPr), and molecular weight (Mw). The PPr and TPr are obtained by calculating the averages of the sample gas critical pressures and critical temperatures. A complicated nonlinear relationship exists between the three influencing variables and the gas density. The data set was divided into a 70:30 ratio for training and testing the model, respectively. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were applied to train and test the model. Absolute average percentage error (AAPE), coefficient of determination (R2), and root mean squared error (RMSE) were considered in the error metrics to acquire the best possible model. Levenberg–Marquardt backpropagation algorithm was employed for ANN, while subtractive clustering was used for ANFIS. Results showed that natural gas density can be well correlated with numerous inputs using machine learning tools (ANN and ANFIS). The input parameters include Ppr, Tpr, and Mw, as mentioned above. ANN performed better than ANFIS. The network was adjusted against the training sub-set to set-up weights and biases covering each node. R2 for both testing and training data was more than 99%, while AAPE was around 4% for both cases. Moreover, a detailed mathematical scheme for the ANN model is also provided in this paper.
{"title":"Deterministic Modeling to Predict the Natural Gas Density Using Artificial Neural Networks","authors":"Mariam Shreif, S. Kalam, Mohammad Rasheed Khan, Rizwan Ahmed Khan","doi":"10.2118/204608-ms","DOIUrl":"https://doi.org/10.2118/204608-ms","url":null,"abstract":"\u0000 During the past decades, several research studies have been made to unfold the immense and diversified benefits of the innovative applications of machine learning (ML) techniques in the petroleum industry. For instance, machine learning algorithms were applied to estimate the various physical properties of natural gas. Natural gas density is considered an indispensable metric that influences the determination of several variables necessary for analyzing natural gas systems. In this work, the Artificial neural network (ANN), a machine learning technique, was applied to estimate natural gas density incorporating the influencing factors. The ANN model was also compared with another ML technique, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS). A mathematical form has been also presented using ANN. A real data set was taken from the literature, comprised of about 4500 data points assimilating three influencing input variables, including pseudo-reduced pressure (PPr), pseudo-reduced temperature (TPr), and molecular weight (Mw). The PPr and TPr are obtained by calculating the averages of the sample gas critical pressures and critical temperatures. A complicated nonlinear relationship exists between the three influencing variables and the gas density. The data set was divided into a 70:30 ratio for training and testing the model, respectively. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were applied to train and test the model. Absolute average percentage error (AAPE), coefficient of determination (R2), and root mean squared error (RMSE) were considered in the error metrics to acquire the best possible model. Levenberg–Marquardt backpropagation algorithm was employed for ANN, while subtractive clustering was used for ANFIS. Results showed that natural gas density can be well correlated with numerous inputs using machine learning tools (ANN and ANFIS). The input parameters include Ppr, Tpr, and Mw, as mentioned above. ANN performed better than ANFIS. The network was adjusted against the training sub-set to set-up weights and biases covering each node. R2 for both testing and training data was more than 99%, while AAPE was around 4% for both cases. Moreover, a detailed mathematical scheme for the ANN model is also provided in this paper.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84459489","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}
Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.
{"title":"First-Break Picking Classification Models Using Recurrent Neural Network","authors":"Mohammed Ayub, S. Kaka","doi":"10.2118/204862-ms","DOIUrl":"https://doi.org/10.2118/204862-ms","url":null,"abstract":"\u0000 Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90353664","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}
Christopher Venske, A. Mohamed, A. Shaban, Nelson Maan, Dr. Colin Hill, Michael. Carroll, R. Findlay
Tatweer Petroleum has been involved in a Pilot study to determine the efficacy of Organic Oil Recovery (OOR), a unique form of microbial enhanced oil recovery as a means of maximising oil recovery from its Rubble reservoir within the Awali field. OOR harnesses microbial life already present in an oil-bearing reservoir to improve oil recovery through changes in interfacial tensions, which in the case of Rubble will increase the heavy oil's mobility and improve recovery rates and reservoir wettability. These changes could increase recoverable reserves and extend field life through improved oil recovery with negligible topsides modifications. The Pilot injection is implemented by injecting a specific nutrient blend directly at the wellhead with ordinary pumping equipment. The well is then shut-in for an incubation period and thereafter returned to production. In Tatweer Petroleum's Awali field the Rubble reservoir is one of the shallowest oil reservoirs in the Bahrain and the first oil discovery in the Gulf Cooperation Council (GCC) region. The reservoir can be found at depths of around 1400 – 1900 ft. During initial laboratory testing of the Rubble target wells the reservoir showed a diverse and abundant resident ecology which has been proven capable of undergoing the necessary characteristic changes to facilitate enhanced production from the target wells. The Pilot test on one of these wells, called Well (A) within this paper, took place in July 2020 and due to this process, the ecology of this well showed these same changes in characteristics in the reservoir along with an associated oil response. The full method of implementation of the Pilot test will also be discussed in detail and will include any challenges and/or successes in this area. The initial state ecology reports of Well (A) are demonstrated and compared to that of post-Pilot test ecology. We also present the production figures for the well prior to and post the Pilot implementation. A correlation will be demonstrated between changes in ecology and an increase in production.
{"title":"Organic Oil Recovery - Resident Microbial Enhanced Production Pilot in Bahrain","authors":"Christopher Venske, A. Mohamed, A. Shaban, Nelson Maan, Dr. Colin Hill, Michael. Carroll, R. Findlay","doi":"10.2118/204884-ms","DOIUrl":"https://doi.org/10.2118/204884-ms","url":null,"abstract":"\u0000 Tatweer Petroleum has been involved in a Pilot study to determine the efficacy of Organic Oil Recovery (OOR), a unique form of microbial enhanced oil recovery as a means of maximising oil recovery from its Rubble reservoir within the Awali field.\u0000 OOR harnesses microbial life already present in an oil-bearing reservoir to improve oil recovery through changes in interfacial tensions, which in the case of Rubble will increase the heavy oil's mobility and improve recovery rates and reservoir wettability. These changes could increase recoverable reserves and extend field life through improved oil recovery with negligible topsides modifications. The Pilot injection is implemented by injecting a specific nutrient blend directly at the wellhead with ordinary pumping equipment. The well is then shut-in for an incubation period and thereafter returned to production.\u0000 In Tatweer Petroleum's Awali field the Rubble reservoir is one of the shallowest oil reservoirs in the Bahrain and the first oil discovery in the Gulf Cooperation Council (GCC) region. The reservoir can be found at depths of around 1400 – 1900 ft. During initial laboratory testing of the Rubble target wells the reservoir showed a diverse and abundant resident ecology which has been proven capable of undergoing the necessary characteristic changes to facilitate enhanced production from the target wells. The Pilot test on one of these wells, called Well (A) within this paper, took place in July 2020 and due to this process, the ecology of this well showed these same changes in characteristics in the reservoir along with an associated oil response. The full method of implementation of the Pilot test will also be discussed in detail and will include any challenges and/or successes in this area. The initial state ecology reports of Well (A) are demonstrated and compared to that of post-Pilot test ecology. We also present the production figures for the well prior to and post the Pilot implementation. A correlation will be demonstrated between changes in ecology and an increase in production.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90784303","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. K. Kamgue Lenwoue, Jin-gen Deng, Yongcun Feng, N. S. Songwe Selabi
Wellbore instability is one of the most important causes of Non-Productive Time causing billions of dollars of losses every year in the petroleum industry. During the drilling operations, the drilling mud is generally utilized to maintain the wellbore stability. However, the drilling mud is subjected to fluctuations caused by several processes such as the drill string vibration cyclic loads which can result into wellbore instability. In this paper, a nonlinear finite element software ABAQUS is utilized as the numerical simulator to evaluate the time dependent pore pressure and stress distribution around the wellbore after integration of drill string vibration cyclic loads. A MATLAB program is then developed to investigate the wellbore stability by computation of the time dependent wellbore collapse pressure and fracture pressure. The numerical results showed that the safe mud window which was initially constant became narrower with the time after integration of vibration cyclic load. The collapse pressure without vibration cyclic load increased by 14.33 % at the final simulation time while the fracture pressure decreased by 13.80 %. Interestingly, the safe mud windows widened with the increase of the normalized wellbore radius as the wellbore fracture pressure increased and the collapse pressure decreased. This study provides an insight into the coupling of the wellbore stability and the continuous cyclic loads generated by drill string vibrations which is an aspect that has been rarely discussed in the literature.
{"title":"Numerical Investigation of the Influence of the Drill String Vibration Cyclic Loads on the Time Dependent Wellbore Stability Analysis","authors":"A. K. Kamgue Lenwoue, Jin-gen Deng, Yongcun Feng, N. S. Songwe Selabi","doi":"10.2118/204774-ms","DOIUrl":"https://doi.org/10.2118/204774-ms","url":null,"abstract":"\u0000 Wellbore instability is one of the most important causes of Non-Productive Time causing billions of dollars of losses every year in the petroleum industry. During the drilling operations, the drilling mud is generally utilized to maintain the wellbore stability. However, the drilling mud is subjected to fluctuations caused by several processes such as the drill string vibration cyclic loads which can result into wellbore instability.\u0000 In this paper, a nonlinear finite element software ABAQUS is utilized as the numerical simulator to evaluate the time dependent pore pressure and stress distribution around the wellbore after integration of drill string vibration cyclic loads. A MATLAB program is then developed to investigate the wellbore stability by computation of the time dependent wellbore collapse pressure and fracture pressure.\u0000 The numerical results showed that the safe mud window which was initially constant became narrower with the time after integration of vibration cyclic load. The collapse pressure without vibration cyclic load increased by 14.33 % at the final simulation time while the fracture pressure decreased by 13.80 %. Interestingly, the safe mud windows widened with the increase of the normalized wellbore radius as the wellbore fracture pressure increased and the collapse pressure decreased.\u0000 This study provides an insight into the coupling of the wellbore stability and the continuous cyclic loads generated by drill string vibrations which is an aspect that has been rarely discussed in the literature.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87256751","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 is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.
{"title":"Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence","authors":"M. Mezghani, Mustafa AlIbrahim, M. Baddourah","doi":"10.2118/204728-ms","DOIUrl":"https://doi.org/10.2118/204728-ms","url":null,"abstract":"\u0000 Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model.\u0000 The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model.\u0000 The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time.\u0000 CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84841607","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}
Oil & gas industry player have always been big investors in advancement of technology, especially in the direction of extracting additional petroleum to address the production decline. In the spirit of automation, PETRONAS has various automated technical workflows that tackles different types of challenges and purposes. The operational, technical and engineering aspects of increasing production and effectiveness of execution are built upon these processes related to automation of data sources as well as systems integration. With the recent challenge that forced the employees to work remotely, it is now more important than ever to ensure that the Digital Fields (DF) solution can cater for more information and to transform the way of working. Linking distant teams to work together on the same platform to resolve production related issues, centralized monitoring and diagnostics is key to this transformation. Workflows can enable organizational vision since having the right type of information available in a visualization environment that provides actionable insights to the right "persona" across different domains and teams accelerates production increases and decreases the production decline at brown fields. The success of this is linked with working together with the Reservoir, Wells and Facilities Management (RWFM) team to ensure the critical information are captured. The improved synergy between offshore and onshore staff due to the shared operations visualization supports further analysis and decision making irrespective of their location. Providing the "persona" with the relevant production and other related data in a modern analytical platform allows them to concentrate on production optimization rather than the data gathering aspect of the traditional method. PETRONAS has considerable experience in developing automated digital oilfield workflow solutions and extending Digital Fields capabilities with greater coverage of other systems such as Health, Safety, and Environment (HSE) and topside facility management is part of the current and future roadmap. In this paper, we will describe the journey taken by PETRONAS Upstream Digital in extending the Digital Fields capability, and how the effort in digital transformation has helped in unlocking greater value in the daily operation.
{"title":"Connecting Reservoir, Wells, Facilities Management, HSEE to Accelerate Data Driven Value: Digital Fields Expansion","authors":"Afdzal Hizamal Abu Bakar, Muhamad Nasri Jamaluddin, Rizwan Musa, Roberto Fuenmayor, Rajesh Trivedi, Mohamad Mustaqim Mokhlis, Muhammad Firdaus Hassan, Ammar Mohamad Azili, Mikhail Harith, Ammar Kamarulzaman","doi":"10.2118/204841-ms","DOIUrl":"https://doi.org/10.2118/204841-ms","url":null,"abstract":"\u0000 Oil & gas industry player have always been big investors in advancement of technology, especially in the direction of extracting additional petroleum to address the production decline.\u0000 In the spirit of automation, PETRONAS has various automated technical workflows that tackles different types of challenges and purposes. The operational, technical and engineering aspects of increasing production and effectiveness of execution are built upon these processes related to automation of data sources as well as systems integration.\u0000 With the recent challenge that forced the employees to work remotely, it is now more important than ever to ensure that the Digital Fields (DF) solution can cater for more information and to transform the way of working. Linking distant teams to work together on the same platform to resolve production related issues, centralized monitoring and diagnostics is key to this transformation.\u0000 Workflows can enable organizational vision since having the right type of information available in a visualization environment that provides actionable insights to the right \"persona\" across different domains and teams accelerates production increases and decreases the production decline at brown fields. The success of this is linked with working together with the Reservoir, Wells and Facilities Management (RWFM) team to ensure the critical information are captured.\u0000 The improved synergy between offshore and onshore staff due to the shared operations visualization supports further analysis and decision making irrespective of their location. Providing the \"persona\" with the relevant production and other related data in a modern analytical platform allows them to concentrate on production optimization rather than the data gathering aspect of the traditional method.\u0000 PETRONAS has considerable experience in developing automated digital oilfield workflow solutions and extending Digital Fields capabilities with greater coverage of other systems such as Health, Safety, and Environment (HSE) and topside facility management is part of the current and future roadmap.\u0000 In this paper, we will describe the journey taken by PETRONAS Upstream Digital in extending the Digital Fields capability, and how the effort in digital transformation has helped in unlocking greater value in the daily operation.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82685462","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 paper is focused on the daily processes in the well kick-off operations, which are still a significant source of risk from occupational safety and health prospect. Several studies show that the number of severe injuries and fatalities still remains high despite substantial efforts the industry has put in recent years in decreasing those numbers. This paper argues that the next level of safety performance will have to consider a transition from coping solely with workplace dangers, to a more innovative paradigm. Taking operations & transportation risks into consideration leads to embracing a smart way to eliminating risk factors to a minimum and, in many cases eliminating such risk.
{"title":"Smart and Innovative Methods Throughout Well Kick-Off Operation","authors":"Ziadat Wael, Arnous Ahmed, Aldabil Abdullah","doi":"10.2118/204899-ms","DOIUrl":"https://doi.org/10.2118/204899-ms","url":null,"abstract":"\u0000 This paper is focused on the daily processes in the well kick-off operations, which are still a significant source of risk from occupational safety and health prospect. Several studies show that the number of severe injuries and fatalities still remains high despite substantial efforts the industry has put in recent years in decreasing those numbers. This paper argues that the next level of safety performance will have to consider a transition from coping solely with workplace dangers, to a more innovative paradigm. Taking operations & transportation risks into consideration leads to embracing a smart way to eliminating risk factors to a minimum and, in many cases eliminating such risk.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85584715","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}
Debashis Konwar, Abhinaba Das, Chandreyi Chatterjee, F. Naim, Chandni Mishra, Sourav Das
Borehole resistivity images and dipole sonic data analysis helps a great deal to identify fractured zones and obtain reasonable estimates of the in-situ stress conditions of geologic formations. Especially when assessing geologic formations for carbon sequestration feasibility, borehole resistivity image and borehole sonic assisted analysis provides answers on presence of fractured zones and stress-state of these fractures. While in deeper formations open fractures would favour carbon storage, in shallower formations, on the other hand, storage integrity would be potentially compromised if these fractures get reactivated, thereby causing induced seismicity due to fluid injection. This paper discusses a methodology adopted to assess the carbon dioxide sequestration feasibility of a formation in the Newark Basin in the United States, using borehole resistivity image(FMI™ Schlumberger) and borehole sonic data (SonicScaner™ Schlumberger). The borehole image was interpreted for the presence of natural and drilling-induced fractures, and also to find the direction of the horizontal stress azimuth from the identified induced fractures. Cross-dipole sonic anisotropy analysis was done to evaluate the presence of intrinsic or stress-based anisotropy in the formation and also to obtain the horizontal stress azimuth. The open or closed nature of natural fractures was deduced from both FMI fracture filling electrical character and the Stoneley reflection wave attenuation from SonicScanner monopole low frequency waveform. The magnitudes of the maximum and minimum horizontal stresses obtained from a 1-Dimensional Mechanical Earth Model were calibrated with stress magnitudes derived from the ‘Integrated Stress Analysis’ approach which takes into account the shear wave radial variation profiles in zones with visible crossover indications of dipole flexural waves. This was followed by a fracture stability analysis in order to identify critically stressed fractures. The borehole resistivity image analysis revealed the presence of abundant natural fractures and microfaults throughout the interval which was also supported by the considerable sonic slowness anisotropy present in those intervals. Stoneley reflected wave attenuation confirmed the openness of some natural fractures identified in the resistivity image. The strike of the natural fractures and microfaults showed an almost NE-SW trend, albeit with considerable variability. The azimuth of maximum horizontal stress obtained in intervals with crossover of dipole flexural waves was also found to be NE-SW in the middle part of the interval, thus coinciding with the overall trend of natural fractures. This might indicate that the stresses in those intervals are also driven by the natural fracture network. However, towards the bottom of the interval, especially from 1255ft-1380ft, where there were indications of drilling induced fractures but no stress-based sonic anisotropy, it was found that that maximum hor
{"title":"Integration of Advanced Borehole Sonic and Resistivity Image Analysis for Fracture and Stress Characterisation - Implications to Carbon Sequestration Feasibility","authors":"Debashis Konwar, Abhinaba Das, Chandreyi Chatterjee, F. Naim, Chandni Mishra, Sourav Das","doi":"10.2118/204696-ms","DOIUrl":"https://doi.org/10.2118/204696-ms","url":null,"abstract":"\u0000 Borehole resistivity images and dipole sonic data analysis helps a great deal to identify fractured zones and obtain reasonable estimates of the in-situ stress conditions of geologic formations. Especially when assessing geologic formations for carbon sequestration feasibility, borehole resistivity image and borehole sonic assisted analysis provides answers on presence of fractured zones and stress-state of these fractures.\u0000 While in deeper formations open fractures would favour carbon storage, in shallower formations, on the other hand, storage integrity would be potentially compromised if these fractures get reactivated, thereby causing induced seismicity due to fluid injection.\u0000 This paper discusses a methodology adopted to assess the carbon dioxide sequestration feasibility of a formation in the Newark Basin in the United States, using borehole resistivity image(FMI™ Schlumberger) and borehole sonic data (SonicScaner™ Schlumberger). The borehole image was interpreted for the presence of natural and drilling-induced fractures, and also to find the direction of the horizontal stress azimuth from the identified induced fractures. Cross-dipole sonic anisotropy analysis was done to evaluate the presence of intrinsic or stress-based anisotropy in the formation and also to obtain the horizontal stress azimuth. The open or closed nature of natural fractures was deduced from both FMI fracture filling electrical character and the Stoneley reflection wave attenuation from SonicScanner monopole low frequency waveform. The magnitudes of the maximum and minimum horizontal stresses obtained from a 1-Dimensional Mechanical Earth Model were calibrated with stress magnitudes derived from the ‘Integrated Stress Analysis’ approach which takes into account the shear wave radial variation profiles in zones with visible crossover indications of dipole flexural waves. This was followed by a fracture stability analysis in order to identify critically stressed fractures.\u0000 The borehole resistivity image analysis revealed the presence of abundant natural fractures and microfaults throughout the interval which was also supported by the considerable sonic slowness anisotropy present in those intervals. Stoneley reflected wave attenuation confirmed the openness of some natural fractures identified in the resistivity image. The strike of the natural fractures and microfaults showed an almost NE-SW trend, albeit with considerable variability. The azimuth of maximum horizontal stress obtained in intervals with crossover of dipole flexural waves was also found to be NE-SW in the middle part of the interval, thus coinciding with the overall trend of natural fractures. This might indicate that the stresses in those intervals are also driven by the natural fracture network. However, towards the bottom of the interval, especially from 1255ft-1380ft, where there were indications of drilling induced fractures but no stress-based sonic anisotropy, it was found that that maximum hor","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81681443","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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