A serious issue facing many oil and gas companies is the uneasiness among the traditional engineering talents to learn and adapt to the changes brought about by digital transformation. The transformation has been expected as the human being is limited in analyzing problems that are multidimensional and there are difficulties in doing analysis on a large scale. But many companies face human factor issues in preparing the traditional staff to realize the potential of adaptation of AI (Artificial Intelligence) based decision making. As decision-making in oil and gas industry is growing in complexity, acceptance of digital based solutions remains low. One reason can be the lack of adequate interpretability. The data scientist and the end-users should be able to assure that the prediction is based on correct set of assumptions and conform to accepted domain expertise knowledge. A proper set of questions to the experts can include inquiries such as where the information comes from, why certain information is pertinent, what is the relationship of components and also would several experts agree on such an assignment. Among many, one of the main concerns is the trustworthiness of applying AI technologies There are limitations of current continuing education approaches, and we suggest improvements that can help in such transformation. It takes an intersection of human judgment and the power of computer technology to make a step-change in accepting predictions by (ML) machine learning. A deep understanding of the problem, coupled with an awareness of the key data, is always the starting point. The best solution strategy in petroleum engineering adaptation of digital technologies requires effective participation of the domain experts in algorithmic-based preprocessing of data. Application of various digital solutions and technologies can then be tested to select the best solution strategies. For illustration purposes, we examine a few examples where digital technologies have significant potentials. Yet in all, domain expertise and data preprocessing are essential for quality control purposes
{"title":"Human Factors in Domain Adaptation Within the Oil and Gas Industry","authors":"I. Ershaghi, Milad A. Ershaghi, Fatimah Al-Ruwai","doi":"10.2118/204820-ms","DOIUrl":"https://doi.org/10.2118/204820-ms","url":null,"abstract":"\u0000 A serious issue facing many oil and gas companies is the uneasiness among the traditional engineering talents to learn and adapt to the changes brought about by digital transformation. The transformation has been expected as the human being is limited in analyzing problems that are multidimensional and there are difficulties in doing analysis on a large scale. But many companies face human factor issues in preparing the traditional staff to realize the potential of adaptation of AI (Artificial Intelligence) based decision making.\u0000 As decision-making in oil and gas industry is growing in complexity, acceptance of digital based solutions remains low. One reason can be the lack of adequate interpretability. The data scientist and the end-users should be able to assure that the prediction is based on correct set of assumptions and conform to accepted domain expertise knowledge. A proper set of questions to the experts can include inquiries such as where the information comes from, why certain information is pertinent, what is the relationship of components and also would several experts agree on such an assignment. Among many, one of the main concerns is the trustworthiness of applying AI technologies\u0000 There are limitations of current continuing education approaches, and we suggest improvements that can help in such transformation. It takes an intersection of human judgment and the power of computer technology to make a step-change in accepting predictions by (ML) machine learning. A deep understanding of the problem, coupled with an awareness of the key data, is always the starting point. The best solution strategy in petroleum engineering adaptation of digital technologies requires effective participation of the domain experts in algorithmic-based preprocessing of data. Application of various digital solutions and technologies can then be tested to select the best solution strategies. For illustration purposes, we examine a few examples where digital technologies have significant potentials. Yet in all, domain expertise and data preprocessing are essential for quality control purposes","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"75185126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents a full waveform sonic (FWS) dataset processing to generate high-resolution images of the near-borehole area. The dataset was acquired in a nearly horizontal well over a distance of 5400 feet. Multiple formation boundaries can be identified on the final image and tracked at up to 200 feet deep, along the wellbore's trajectory. We first present a new preprocessing sequence to prepare the sonic data for imaging. This sequence leverages denoising algorithms used in conventional surface seismic data processing to remove unwanted components of the recorded data that could harm the imaging results. We then apply a reverse time migration algorithm to the data at different processing stages to assess the impact of the main processing steps on the final image.
{"title":"Near-Borehole Imaging Using Full-Waveform Sonic Data","authors":"Hala Alqatari, T. Tonellot, M. Mubarak","doi":"10.2118/204765-ms","DOIUrl":"https://doi.org/10.2118/204765-ms","url":null,"abstract":"\u0000 This work presents a full waveform sonic (FWS) dataset processing to generate high-resolution images of the near-borehole area. The dataset was acquired in a nearly horizontal well over a distance of 5400 feet. Multiple formation boundaries can be identified on the final image and tracked at up to 200 feet deep, along the wellbore's trajectory.\u0000 We first present a new preprocessing sequence to prepare the sonic data for imaging. This sequence leverages denoising algorithms used in conventional surface seismic data processing to remove unwanted components of the recorded data that could harm the imaging results. We then apply a reverse time migration algorithm to the data at different processing stages to assess the impact of the main processing steps on the final image.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"75572971","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}
Multilateral wells are an evolution of horizontal wells in which several wellbore branches radiate from the main borehole. In the last two decades, multilateral wells have been increasingly utilized in producing hydrocarbon reservoirs. The main advantage of using such technology against conventional and single-bore wells comes from the additional access to reservoir rock by maximizing the reservoir contact with fewer resources. Today, multilateral wells are rapidly becoming more complex in both designs and architecture (i.e., extended reach wells, maximum reservoir contact, and extreme reservoir contact wells). Certain multilateral design templates prevail in the industry, such as fork and fishbone types, which tend to be populated throughout the reservoir of interest with no significant changes to the original architecture and, therefore, may not fully realize the reservoir's potential. Placement of optimal multilateral wells is a multivariable problem, which is a function of determining the best well locations and trajectories in a hydrocarbon reservoir with the ultimate objectives of maximizing productivity and recovery. The placement of the multilateral wells can be subject to many constraints such as the number of wells required, maximum length limits, and overall economics. This paper introduces a novel technology for placement of multilateral wells in hydrocarbon reservoirs utilizing a transshipment network optimization approach. This method generates scenarios of multiple wells with different designs honoring the most favorable completion points in a reservoir. In addition, the algorithm was developed to find the most favorable locations and trajectories for the multilateral wells in both local and global terms. A partitioning algorithm is uniquely utilized to reduce the computational cost of the process. The proposed method will not only create different multilateral designs; it will justify the trajectories of every borehole section generated. The innovative method is capable of constructing hundreds of multilateral wells with design variations in large-scale reservoirs. As the complexity of the reservoirs (e.g., active forces that influence fluid mobility) and heterogeneity dictate variability in performance at different area of the reservoir, multilateral wells should be constructed to capture the most productive zones. The new method also allows different levels of branching for the laterals (i.e., laterals can emanate from the motherbore, from other laterals or from subsequent branches). These features set the stage for a new generation of multilateral wells to achieve the most effective reservoir contact.
{"title":"Large Scale Placement For Multilateral Wells Using Network Optimization","authors":"G. Al-Qahtani, Noah E. Berlow","doi":"10.2118/204803-ms","DOIUrl":"https://doi.org/10.2118/204803-ms","url":null,"abstract":"\u0000 Multilateral wells are an evolution of horizontal wells in which several wellbore branches radiate from the main borehole. In the last two decades, multilateral wells have been increasingly utilized in producing hydrocarbon reservoirs. The main advantage of using such technology against conventional and single-bore wells comes from the additional access to reservoir rock by maximizing the reservoir contact with fewer resources. Today, multilateral wells are rapidly becoming more complex in both designs and architecture (i.e., extended reach wells, maximum reservoir contact, and extreme reservoir contact wells). Certain multilateral design templates prevail in the industry, such as fork and fishbone types, which tend to be populated throughout the reservoir of interest with no significant changes to the original architecture and, therefore, may not fully realize the reservoir's potential. Placement of optimal multilateral wells is a multivariable problem, which is a function of determining the best well locations and trajectories in a hydrocarbon reservoir with the ultimate objectives of maximizing productivity and recovery. The placement of the multilateral wells can be subject to many constraints such as the number of wells required, maximum length limits, and overall economics.\u0000 This paper introduces a novel technology for placement of multilateral wells in hydrocarbon reservoirs utilizing a transshipment network optimization approach. This method generates scenarios of multiple wells with different designs honoring the most favorable completion points in a reservoir. In addition, the algorithm was developed to find the most favorable locations and trajectories for the multilateral wells in both local and global terms. A partitioning algorithm is uniquely utilized to reduce the computational cost of the process. The proposed method will not only create different multilateral designs; it will justify the trajectories of every borehole section generated.\u0000 The innovative method is capable of constructing hundreds of multilateral wells with design variations in large-scale reservoirs. As the complexity of the reservoirs (e.g., active forces that influence fluid mobility) and heterogeneity dictate variability in performance at different area of the reservoir, multilateral wells should be constructed to capture the most productive zones. The new method also allows different levels of branching for the laterals (i.e., laterals can emanate from the motherbore, from other laterals or from subsequent branches). These features set the stage for a new generation of multilateral wells to achieve the most effective reservoir contact.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"72609964","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}
Running two compression set tools in a single wellbore clean out string, typically a bypass tool and negative test packer, has been a significant industry challenge to operate reliably. The need for running these types of tools is generally driven by the need to perform a negative test on a liner top and achieve high flow rates necessary to hydraulically remove debris from the well. Combining these operations into a single run is an increasingly common method to reduce rig time and cost for the operator. Tools to perform this type of operation are generally available from many service providers, however difficulties and challenges arise when trying to manipulate two different tools in the same string that function by the same compression set method. These operations do have a history that is partially successful, however on a long term basis reliability is generally considered poor by most operators, as a failure to manipulate the tools correctly can result in a failed run and a trip out of the hole. This paper discusses the development and successful field deployment of a system of two compression set tools to address this specific challenge while improving reliability over existing solutions.
{"title":"Development and Implementation of Two Compression Set Tools in a Wellbore Clean Out String","authors":"P. Maher, Carl Nelson, D. Dockweiler","doi":"10.2118/204664-ms","DOIUrl":"https://doi.org/10.2118/204664-ms","url":null,"abstract":"\u0000 Running two compression set tools in a single wellbore clean out string, typically a bypass tool and negative test packer, has been a significant industry challenge to operate reliably. The need for running these types of tools is generally driven by the need to perform a negative test on a liner top and achieve high flow rates necessary to hydraulically remove debris from the well. Combining these operations into a single run is an increasingly common method to reduce rig time and cost for the operator. Tools to perform this type of operation are generally available from many service providers, however difficulties and challenges arise when trying to manipulate two different tools in the same string that function by the same compression set method. These operations do have a history that is partially successful, however on a long term basis reliability is generally considered poor by most operators, as a failure to manipulate the tools correctly can result in a failed run and a trip out of the hole. This paper discusses the development and successful field deployment of a system of two compression set tools to address this specific challenge while improving reliability over existing solutions.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"78395588","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}
Klemens Katterbauer, A. Marsala, Yanhui Zhang, I. Hoteit
Facies classification for complex reservoirs is an important step in characterizing reservoir heterogeneity and determining reservoir properties and fluid flow patterns. Predicting rock facies automatically and reliably from well log and associated reservoir measurements is therefore essential to obtain accurate reservoir characterization for field development in a timely manner. In this study, we present an artificial intelligence (AI) aided rock facies classification framework for complex reservoirs based on well log measurements. We generalize the AI-aided classification workflow into five major steps including data collection, preprocessing, feature engineering, model learning cycle, and model prediction. In particular, we automate the process of facies classification focusing on the use of a deep learning technique, convolutional neural network, which has shown outstanding performance in many scientific applications involving pattern recognition and classification. For performance analysis, we also compare the developed model with a support vector machine approach. We examine the AI-aided workflow on a large open dataset acquired from a real complex reservoir in Alberta. The dataset contains a collection of well-log measurements over a couple of thousands of wells. The experimental results demonstrate the high efficiency and scalability of the developed framework for automatic facies classification with reasonable accuracy. This is particularly useful when quick facies prediction is necessary to support real-time decision making. The AI-aided framework is easily implementable and expandable to other reservoir applications.
{"title":"Artificial Intelligence Aided Geologic Facies Classification in Complex Carbonate Reservoirs","authors":"Klemens Katterbauer, A. Marsala, Yanhui Zhang, I. Hoteit","doi":"10.2118/204705-ms","DOIUrl":"https://doi.org/10.2118/204705-ms","url":null,"abstract":"\u0000 Facies classification for complex reservoirs is an important step in characterizing reservoir heterogeneity and determining reservoir properties and fluid flow patterns. Predicting rock facies automatically and reliably from well log and associated reservoir measurements is therefore essential to obtain accurate reservoir characterization for field development in a timely manner.\u0000 In this study, we present an artificial intelligence (AI) aided rock facies classification framework for complex reservoirs based on well log measurements. We generalize the AI-aided classification workflow into five major steps including data collection, preprocessing, feature engineering, model learning cycle, and model prediction. In particular, we automate the process of facies classification focusing on the use of a deep learning technique, convolutional neural network, which has shown outstanding performance in many scientific applications involving pattern recognition and classification. For performance analysis, we also compare the developed model with a support vector machine approach.\u0000 We examine the AI-aided workflow on a large open dataset acquired from a real complex reservoir in Alberta. The dataset contains a collection of well-log measurements over a couple of thousands of wells. The experimental results demonstrate the high efficiency and scalability of the developed framework for automatic facies classification with reasonable accuracy. This is particularly useful when quick facies prediction is necessary to support real-time decision making. The AI-aided framework is easily implementable and expandable to other reservoir applications.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"78421628","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}
Syamil Mohd Razak, J. Cornelio, Atefeh Jahandideh, B. Jafarpour, Young Cho, Hui-Hai Liu, R. Vaidya
The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs are not well understood. As a result, the predicted production behavior using conventional simulation often does not agree with the observed field performance data. The discrepancy is caused by potential errors in the simulation model and the physical processes that take place in complex fractured rocks subjected to hydraulic fracturing. Additionally, other field data such as well logs and drilling parameters containing important information about reservoir condition and reservoir characteristics are not conveniently integrated into existing simulation models. In this paper, we discuss the development of a deep learning model to learn the errors in simulation-based performance prediction in unconventional reservoirs. Once trained, the model is expected to forecast the performance response of a well by augmenting physics-based predictions with the learned prediction errors from the deep learning model. To learn the discrepancy between simulated and observed production data, a simulation dataset is generated by using formation, completion, and fluid properties as input to an imperfect physics-based simulation model. The difference between the resulting simulated responses and observed field data, together with collected field data (i.e. well logs, drilling parameters), is then used to train a deep learning model to learn the prediction errors of the imperfect physical model. Deep convolutional autoencoder architectures are used to map the simulated and observed production responses into a low-dimensional manifold, where a regression model is trained to learn the mapping between collected field data and the simulated data in the latent space. The proposed method leverages deep learning models to account for prediction errors originating from potentially missing physical phenomena, simulation inputs, and reservoir description. We illustrate our approach using a case study from the Bakken Play in North Dakota.
{"title":"Integrating Deep Learning and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs","authors":"Syamil Mohd Razak, J. Cornelio, Atefeh Jahandideh, B. Jafarpour, Young Cho, Hui-Hai Liu, R. Vaidya","doi":"10.2118/204864-ms","DOIUrl":"https://doi.org/10.2118/204864-ms","url":null,"abstract":"\u0000 The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs are not well understood. As a result, the predicted production behavior using conventional simulation often does not agree with the observed field performance data. The discrepancy is caused by potential errors in the simulation model and the physical processes that take place in complex fractured rocks subjected to hydraulic fracturing. Additionally, other field data such as well logs and drilling parameters containing important information about reservoir condition and reservoir characteristics are not conveniently integrated into existing simulation models. In this paper, we discuss the development of a deep learning model to learn the errors in simulation-based performance prediction in unconventional reservoirs. Once trained, the model is expected to forecast the performance response of a well by augmenting physics-based predictions with the learned prediction errors from the deep learning model. To learn the discrepancy between simulated and observed production data, a simulation dataset is generated by using formation, completion, and fluid properties as input to an imperfect physics-based simulation model. The difference between the resulting simulated responses and observed field data, together with collected field data (i.e. well logs, drilling parameters), is then used to train a deep learning model to learn the prediction errors of the imperfect physical model. Deep convolutional autoencoder architectures are used to map the simulated and observed production responses into a low-dimensional manifold, where a regression model is trained to learn the mapping between collected field data and the simulated data in the latent space. The proposed method leverages deep learning models to account for prediction errors originating from potentially missing physical phenomena, simulation inputs, and reservoir description. We illustrate our approach using a case study from the Bakken Play in North Dakota.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"77386894","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}
Rizwan Ahmed Khan, Mobeen Murtaza, H. Ahmad, A. Abdulraheem, M. Kamal, M. Mahmoud
In the last decade, hydrophilic Ionic liquids have been emerged as an additive in drilling fluids for clay swelling inhibition. However, the application of hydrophobic Ionic liquids as a clay swelling inhibitor have not been investigated. In this study, the combination of hydrophobic Ionic liquids and Gemini surfactant were studied to evaluate the inhibition performance. The novel combination of hydrophobic ionic liquid (Trihexyltetradecyl phosphonium bis(2,4,4-trimethyl pentyl) phosphinate) and cationic gemini surfactant (GB) was prepared by mixing various concentrations of both chemicals and then preparing water based drilling fluid using other drilling fluid additives such as rheological modifier, filtration control agent, and pH control agent. The wettability of sodium bentonite was determined by contact angle with different concentrations of combined solution. Some other experiments such as linear swelling, capillary suction test (CST) and bentonite swell index were performed to study the inhibition performance of ionic liquid. Different concentrations of novel combined ionic liquid and gemini surfactant were used to prepare the drilling fluids ranging from (0.1 to 0.5 wt.%), and their performances were compared with the base drilling fluid. The wettability results showed that novel drilling fluid having 0.1% Tpb-P - 0.5% GB wt.% concentration has a maximum contact angle indicating the highly hydrophobic surface. The linear swelling was evaluated over the time of 24 hours, and least swelling of bentonite was noticed with 0.1% Tpb-P - 0.5% GB wt.% combined solution compared to linear swelling in deionized water. Furthermore, the results of CST also suggested the improved performance of novel solution at 0.1% Tpb-P - 0.1% GB concentration. The novel combination The novel combination of hydrophobic ionic liquids and gemini surfactant has been used to formulate the drilling fluid for high temperature applications to modify the wettability and hydration properties of clay. The use of novel combined ionic liquid and gemini surfactant improves the borehole stability by adjusting the clay surface and resulted in upgraded wellbore stability.
{"title":"Development of Novel Shale Swelling Inhibitors Using Hydrophobic Ionic Liquids and Gemini Surfactants for Water-Based Drilling Fluids","authors":"Rizwan Ahmed Khan, Mobeen Murtaza, H. Ahmad, A. Abdulraheem, M. Kamal, M. Mahmoud","doi":"10.2118/204740-ms","DOIUrl":"https://doi.org/10.2118/204740-ms","url":null,"abstract":"\u0000 In the last decade, hydrophilic Ionic liquids have been emerged as an additive in drilling fluids for clay swelling inhibition. However, the application of hydrophobic Ionic liquids as a clay swelling inhibitor have not been investigated. In this study, the combination of hydrophobic Ionic liquids and Gemini surfactant were studied to evaluate the inhibition performance.\u0000 The novel combination of hydrophobic ionic liquid (Trihexyltetradecyl phosphonium bis(2,4,4-trimethyl pentyl) phosphinate) and cationic gemini surfactant (GB) was prepared by mixing various concentrations of both chemicals and then preparing water based drilling fluid using other drilling fluid additives such as rheological modifier, filtration control agent, and pH control agent. The wettability of sodium bentonite was determined by contact angle with different concentrations of combined solution. Some other experiments such as linear swelling, capillary suction test (CST) and bentonite swell index were performed to study the inhibition performance of ionic liquid.\u0000 Different concentrations of novel combined ionic liquid and gemini surfactant were used to prepare the drilling fluids ranging from (0.1 to 0.5 wt.%), and their performances were compared with the base drilling fluid. The wettability results showed that novel drilling fluid having 0.1% Tpb-P - 0.5% GB wt.% concentration has a maximum contact angle indicating the highly hydrophobic surface. The linear swelling was evaluated over the time of 24 hours, and least swelling of bentonite was noticed with 0.1% Tpb-P - 0.5% GB wt.% combined solution compared to linear swelling in deionized water. Furthermore, the results of CST also suggested the improved performance of novel solution at 0.1% Tpb-P - 0.1% GB concentration. The novel combination\u0000 The novel combination of hydrophobic ionic liquids and gemini surfactant has been used to formulate the drilling fluid for high temperature applications to modify the wettability and hydration properties of clay. The use of novel combined ionic liquid and gemini surfactant improves the borehole stability by adjusting the clay surface and resulted in upgraded wellbore stability.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"77633895","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}
Hang Su, Fu-jian Zhou, Lida Wang, Chuan Wang, Lixia Kang, Fuwei Yu, Junjian Li
For reservoirs containing oil with a high total acid number, alkali-cosolvent-polymer (ACP) flood can potentially increase the oil recovery by its saponification effects. The enhanced oil recovery performance of ACP flood has been studied at core and reservoir scale in detail, however, the effect of ACP flood on residual oil saturation in the swept area still lacks enough research. Medical computed tomography (Medical-CT) scan and micro computed tomography (Micro-CT) scan are used in combination to visualize micro-scale flow and reveal the mechanisms of residual oil reduction during ACP flood. The heterogeneous cores containing two layers of different permeability are used for coreflood experiment to clarify the enhanced oil recovery (EOR) performance of ACP food in heterogeneous reservoirs. The oil saturation is monitored by Medical-CT. Then, two core samples are drilled in each core after flooding and the decrease of residual oil saturation caused by ACP flood is further quantified by Micro-CT imageing. Results show that ACP flood is 14.5% oil recovery higher than alkaline-cosolvent (AC) flood (68.9%) in high permeability layers, 17.9% higher than AC flood (26.3%) in low permeability layers. Compared with AC flood, ACP flood shows a more uniform displacement front, which implies that the injected polymer effectively weakened the viscosity fingering. Moreover, a method that can calculate the ratio of oil-water distribution in each pore is developed to establish the relationship between the residual oil saturation of each pore and its pore size, and reached the conclusion that they follow the power law correlation.
{"title":"Heavy Oil Recovery by Alkaline-Cosolvent-Polymer Flood: A Multi-Scale Research Using CT Imaging","authors":"Hang Su, Fu-jian Zhou, Lida Wang, Chuan Wang, Lixia Kang, Fuwei Yu, Junjian Li","doi":"10.2118/204766-ms","DOIUrl":"https://doi.org/10.2118/204766-ms","url":null,"abstract":"\u0000 For reservoirs containing oil with a high total acid number, alkali-cosolvent-polymer (ACP) flood can potentially increase the oil recovery by its saponification effects. The enhanced oil recovery performance of ACP flood has been studied at core and reservoir scale in detail, however, the effect of ACP flood on residual oil saturation in the swept area still lacks enough research. Medical computed tomography (Medical-CT) scan and micro computed tomography (Micro-CT) scan are used in combination to visualize micro-scale flow and reveal the mechanisms of residual oil reduction during ACP flood. The heterogeneous cores containing two layers of different permeability are used for coreflood experiment to clarify the enhanced oil recovery (EOR) performance of ACP food in heterogeneous reservoirs. The oil saturation is monitored by Medical-CT. Then, two core samples are drilled in each core after flooding and the decrease of residual oil saturation caused by ACP flood is further quantified by Micro-CT imageing. Results show that ACP flood is 14.5% oil recovery higher than alkaline-cosolvent (AC) flood (68.9%) in high permeability layers, 17.9% higher than AC flood (26.3%) in low permeability layers. Compared with AC flood, ACP flood shows a more uniform displacement front, which implies that the injected polymer effectively weakened the viscosity fingering. Moreover, a method that can calculate the ratio of oil-water distribution in each pore is developed to establish the relationship between the residual oil saturation of each pore and its pore size, and reached the conclusion that they follow the power law correlation.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"80236669","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 challenging offshore drilling environment has increased the need for cost-effective operations to deliver accurate well placement, high borehole quality, and shoe-to-shoe drilling performance. As well construction complexity continues to develop, the need for an improved systems approach to delivering integrated performance is critical. Complex bottom hole assemblies (BHA) used in deepwater operations will include additional sensors and capabilities than in the past. These BHAs consist of multiple cutting structures (bit/reamer), gamma, resistivity, density, porosity, sonic, formation pressure testing/sampling capabilities, as well as drilling dynamics systems and onboard diagnostic sensors. Rock cutting structure design primarily relied on data capture at the surface. An instrumented sensor package within the drill bit provides dynamic measurements allowing for better understanding of BHA performance, creating a more efficient system for all drilling conditions. The addition of intelligent systems that monitor and control these complex BHAs, makes it possible to implement autonomous steering of directional drilling assemblies in the offshore environment. In the Deepwater Gulf of Mexico (GOM), this case study documents the introduction of a new automated drilling service and Intelligent Rotary Steerable System (iRSS) with an instrumented bit. Utilizing these complex BHAs, the system can provide real-time (RT) steering decisions automatically given the downhole tool configuration, planned well path, and RT sensor information received. The 6-3/4-inch nominal diameter system, coupled with the instrumented bit, successfully completed the first 5,400-foot (1,650m) section while enlarging the 8-1/2-inch (216mm) borehole to 9-7/8 inches (250mm). The system delivered a high-quality wellbore with low tortuosity and minimal vibration, while keeping to the planned well path. The system achieved all performance objectives and captured dynamic drilling responses for use in an additional applications. This fast sampling iRSS maintains continuous and faster steering control at high rates of penetration (ROP) providing accurate well path directional control. The system-matched polycrystalline diamond (PDC) bit is engineered to deliver greater side cutting efficiency with enhanced cutting structure improving the iRSS performance. Included within the bit is an instrumentation package that tracks drilling dynamics at the bit. The bit dynamics data is then used to improve bit designs and optimize drilling parameters.
{"title":"Intelligent Rotary Steerable System, Coupled with an Instrumented Bit, Delivers Section Plan in Deepwater GOM Project","authors":"J. Snyder, G. Salmon","doi":"10.2118/204680-ms","DOIUrl":"https://doi.org/10.2118/204680-ms","url":null,"abstract":"\u0000 The challenging offshore drilling environment has increased the need for cost-effective operations to deliver accurate well placement, high borehole quality, and shoe-to-shoe drilling performance. As well construction complexity continues to develop, the need for an improved systems approach to delivering integrated performance is critical.\u0000 Complex bottom hole assemblies (BHA) used in deepwater operations will include additional sensors and capabilities than in the past. These BHAs consist of multiple cutting structures (bit/reamer), gamma, resistivity, density, porosity, sonic, formation pressure testing/sampling capabilities, as well as drilling dynamics systems and onboard diagnostic sensors. Rock cutting structure design primarily relied on data capture at the surface. An instrumented sensor package within the drill bit provides dynamic measurements allowing for better understanding of BHA performance, creating a more efficient system for all drilling conditions. The addition of intelligent systems that monitor and control these complex BHAs, makes it possible to implement autonomous steering of directional drilling assemblies in the offshore environment.\u0000 In the Deepwater Gulf of Mexico (GOM), this case study documents the introduction of a new automated drilling service and Intelligent Rotary Steerable System (iRSS) with an instrumented bit. Utilizing these complex BHAs, the system can provide real-time (RT) steering decisions automatically given the downhole tool configuration, planned well path, and RT sensor information received. The 6-3/4-inch nominal diameter system, coupled with the instrumented bit, successfully completed the first 5,400-foot (1,650m) section while enlarging the 8-1/2-inch (216mm) borehole to 9-7/8 inches (250mm). The system delivered a high-quality wellbore with low tortuosity and minimal vibration, while keeping to the planned well path. The system achieved all performance objectives and captured dynamic drilling responses for use in an additional applications.\u0000 This fast sampling iRSS maintains continuous and faster steering control at high rates of penetration (ROP) providing accurate well path directional control. The system-matched polycrystalline diamond (PDC) bit is engineered to deliver greater side cutting efficiency with enhanced cutting structure improving the iRSS performance. Included within the bit is an instrumentation package that tracks drilling dynamics at the bit. The bit dynamics data is then used to improve bit designs and optimize drilling parameters.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"81532993","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}
L. Camilleri, Mohammed Al-Jorani, Mohammed Kamal Aal Najar, J. Ayoub
While pressure transient analysis (PTA) is a proven interpretation technique, it is mostly used on buildups because drawdowns are difficult to interpret. However, the deferred production associated with buildups discourages regular application of PTA to determine skin and identify boundary conditions. Several case studies are presented covering a range of well configurations to illustrate how downhole transient liquid rate measurements with electrical submersible pump (ESP) gauges enable PTA during drawdown and therefore real-time optimization. The calculation of high-frequency transient flow rates using ESP gauge real-time data is based on the principle that the power absorbed by the pump is equal to that generated by the motor. This technique is independent of fluid specific gravity and therefore is self-calibrating with changes in water cut and phase segregation. Analytical equations ensure that the physics is always respected, thereby providing the necessary repeatability. The combination of downhole transient high-frequency flow rate and permanent pressure gauge data enables PTA using commonly available analytical techniques and software, especially because superposition time is calculated accurately. The availability of continuous production history brings significant value for PTA. It makes it possible to perform history matching and to deploy semilog analysis using an accurate set of superposition time functions. However, the application of log-log analysis techniques is usually more challenging because of imperfections in input data such as noise, oversimplified production history, time-synchronization issues, or wellbore effects. These limitations are solved by utilizing high-frequency downhole data from ESP. This is possible first as superposition time is effectively an integral function, which dampens any noise in the flow rate signal. Another important finding is that wellbore effects in subhydrostatic wells are less impactful in drawdowns than in buildups where compressibility and redistribution can mask reservoir response. Key reservoir properties, in particular mobility, can nearly always be estimated, leading to better skin factor determination even without downhole shut-in. Finally, with the constraint of production deferment eliminated, drawdowns can be monitored for extended durations to identify boundaries and to perform time-lapse interpretation more efficiently. Confirming a constant pressure boundary or a change in skin enables more effective and proactive production management. In all cases considered, a complete analysis was possible, including buildup and drawdown data comparison. With the development of downhole flow rate calculation technology, it is now possible to provide full inflow characterization in a matter of days following an ESP workover, without any additional hardware or staff mobilization to the wellsite and no deferred production. More importantly, the technique provides the necessary information to
{"title":"Delivering Pressure Transient Analysis During Drawdown on ESP Wells: Case Studies and Lessons Learned","authors":"L. Camilleri, Mohammed Al-Jorani, Mohammed Kamal Aal Najar, J. Ayoub","doi":"10.2118/204567-ms","DOIUrl":"https://doi.org/10.2118/204567-ms","url":null,"abstract":"\u0000 While pressure transient analysis (PTA) is a proven interpretation technique, it is mostly used on buildups because drawdowns are difficult to interpret. However, the deferred production associated with buildups discourages regular application of PTA to determine skin and identify boundary conditions. Several case studies are presented covering a range of well configurations to illustrate how downhole transient liquid rate measurements with electrical submersible pump (ESP) gauges enable PTA during drawdown and therefore real-time optimization.\u0000 The calculation of high-frequency transient flow rates using ESP gauge real-time data is based on the principle that the power absorbed by the pump is equal to that generated by the motor. This technique is independent of fluid specific gravity and therefore is self-calibrating with changes in water cut and phase segregation. Analytical equations ensure that the physics is always respected, thereby providing the necessary repeatability. The combination of downhole transient high-frequency flow rate and permanent pressure gauge data enables PTA using commonly available analytical techniques and software, especially because superposition time is calculated accurately.\u0000 The availability of continuous production history brings significant value for PTA. It makes it possible to perform history matching and to deploy semilog analysis using an accurate set of superposition time functions. However, the application of log-log analysis techniques is usually more challenging because of imperfections in input data such as noise, oversimplified production history, time-synchronization issues, or wellbore effects. These limitations are solved by utilizing high-frequency downhole data from ESP. This is possible first as superposition time is effectively an integral function, which dampens any noise in the flow rate signal. Another important finding is that wellbore effects in subhydrostatic wells are less impactful in drawdowns than in buildups where compressibility and redistribution can mask reservoir response. Key reservoir properties, in particular mobility, can nearly always be estimated, leading to better skin factor determination even without downhole shut-in. Finally, with the constraint of production deferment eliminated, drawdowns can be monitored for extended durations to identify boundaries and to perform time-lapse interpretation more efficiently. Confirming a constant pressure boundary or a change in skin enables more effective and proactive production management. In all cases considered, a complete analysis was possible, including buildup and drawdown data comparison.\u0000 With the development of downhole flow rate calculation technology, it is now possible to provide full inflow characterization in a matter of days following an ESP workover, without any additional hardware or staff mobilization to the wellsite and no deferred production. More importantly, the technique provides the necessary information to","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 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":"81692968","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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