Bernard Chang, Javier E. Santos, R. Victor, H. Viswanathan, M. Prodanović
Imaging technology is constantly improving and enabling accurate, deterministic simulations of transport properties through the pore space of the imaged rock sample. Meanwhile, data-driven machine learning has emerged as an alternate tool for modeling transport properties that, once trained, use a fraction of the computational resources that traditional simulations require. However, machine learning models often fail to strictly enforce the physical constraints of the system, leading to solutions that are less accurate than that of traditional solvers. Here we propose a novel hybrid workflow that combines machine learning and conventional simulation methods. The workflow begins with a three-dimensional, binary image of a sample. A trained convolutional neural network extracts spatial relationships between the porous medium geometry and the electrostatic potential field and predicts the electrical properties through a new medium. Instead of assuming a linear potential gradient, this prediction is used as the initial condition of a validated finite difference solver. The implementation of this workflow can improve the simulation run time by an order of magnitude for small images. The success of the proposed workflow heavily depends on the accuracy of model prediction. We previously developed successful methods for prediction of the velocity field (and permeability) of a Newtonian fluid in a porous medium in the laminar regime. Here, we extend the method to predict the electrical potential field. We explore one strategy of improving a model's ability to generalize to unseen samples by supplying geometric characterizations of the pore space. We find that models trained with these features individually do not result in an improvement over the baseline model trained with only the binary image. However, they do provide the model with relational information that can be incorporated into future models. Analysis of electrical properties is one of the most common methods of delineating hydrocarbon saturation in reservoir rock. The proposed workflow helps accelerate the calculation of the electric potential field and can lead to estimating hydrocarbon saturation in real time. We also expect that this workflow is easily generalized to many other transport problems in porous media.
{"title":"Improving Machine Learning Predictions of Rock Electric Properties Using 3D Geometric Features","authors":"Bernard Chang, Javier E. Santos, R. Victor, H. Viswanathan, M. Prodanović","doi":"10.2118/210456-ms","DOIUrl":"https://doi.org/10.2118/210456-ms","url":null,"abstract":"\u0000 Imaging technology is constantly improving and enabling accurate, deterministic simulations of transport properties through the pore space of the imaged rock sample. Meanwhile, data-driven machine learning has emerged as an alternate tool for modeling transport properties that, once trained, use a fraction of the computational resources that traditional simulations require. However, machine learning models often fail to strictly enforce the physical constraints of the system, leading to solutions that are less accurate than that of traditional solvers.\u0000 Here we propose a novel hybrid workflow that combines machine learning and conventional simulation methods. The workflow begins with a three-dimensional, binary image of a sample. A trained convolutional neural network extracts spatial relationships between the porous medium geometry and the electrostatic potential field and predicts the electrical properties through a new medium. Instead of assuming a linear potential gradient, this prediction is used as the initial condition of a validated finite difference solver. The implementation of this workflow can improve the simulation run time by an order of magnitude for small images.\u0000 The success of the proposed workflow heavily depends on the accuracy of model prediction. We previously developed successful methods for prediction of the velocity field (and permeability) of a Newtonian fluid in a porous medium in the laminar regime. Here, we extend the method to predict the electrical potential field. We explore one strategy of improving a model's ability to generalize to unseen samples by supplying geometric characterizations of the pore space. We find that models trained with these features individually do not result in an improvement over the baseline model trained with only the binary image. However, they do provide the model with relational information that can be incorporated into future models.\u0000 Analysis of electrical properties is one of the most common methods of delineating hydrocarbon saturation in reservoir rock. The proposed workflow helps accelerate the calculation of the electric potential field and can lead to estimating hydrocarbon saturation in real time. We also expect that this workflow is easily generalized to many other transport problems in porous media.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128222075","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}
In this study, we present a framework for efficient estimation of the optimal CO2-WAG parameters for robust production-optimization problems by replacing a high-fidelity model with a least-squares support vector regression (LS-SVR) model. We provide insight and information on proper training of the LS-SVR proxy model for the CO2-WAG life-cycle production optimization problem. Given a set of training points generated from high-fidelity model-based simulation results, an LS-SVR-based proxy model is built to approximate a reservoir-simulation model. The estimated optimal design parameters are then found by maximizing NPV using the LS-SVR proxy as the forward model within an iterative-sampling-refinement optimization algorithm that is designed specifically to promote the accuracy of the proxy model for robust production optimization. As an optimization tool, the sequential quadratic programming (SQP) method is used. CO2-WAG design variables are CO2 injection and water injection rates for each injection well at each cycle, production BHP for each production well at each WAG half-cycle, and inflow control valve (ICV) for each well at each WAG half-cycle and at each valve. We study different scenarios where we fix some of the design variables to investigate the importance of design variables on life-cycle production optimization of the CO2-WAG problem. We compare the performance of the proposed method using the LS-SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for a synthetic example considering a three-layer, channelized reservoir with 4 injectors and 9 producers. Results show that the proposed LS-SVR-based optimization framework is at least 3 to 6 times computationally more efficient, depending on the cases considered, than the StoSAG using a high-fidelity numerical simulator. However, we observe that the size and sampling of the training data, as well as the selection of well controls and their bound constraints for the well controls, seem to be influential on the performance of the LS-SVR-based optimization method. This is the first LS-SVR application to the CO2-WAG optimal well-control problem. The proposed LS-SVR-based optimization framework has great potential to be used as an efficient tool for the CO2-WAG optimization problem.
{"title":"Life-Cycle Production Optimization of the CO2-Water-Alternating-Gas Injection Process Using Least-Squares Support-Vector Regression (LS-SVR) Proxy","authors":"A. Almasov, M. Onur","doi":"10.2118/210200-ms","DOIUrl":"https://doi.org/10.2118/210200-ms","url":null,"abstract":"\u0000 In this study, we present a framework for efficient estimation of the optimal CO2-WAG parameters for robust production-optimization problems by replacing a high-fidelity model with a least-squares support vector regression (LS-SVR) model. We provide insight and information on proper training of the LS-SVR proxy model for the CO2-WAG life-cycle production optimization problem. Given a set of training points generated from high-fidelity model-based simulation results, an LS-SVR-based proxy model is built to approximate a reservoir-simulation model. The estimated optimal design parameters are then found by maximizing NPV using the LS-SVR proxy as the forward model within an iterative-sampling-refinement optimization algorithm that is designed specifically to promote the accuracy of the proxy model for robust production optimization. As an optimization tool, the sequential quadratic programming (SQP) method is used. CO2-WAG design variables are CO2 injection and water injection rates for each injection well at each cycle, production BHP for each production well at each WAG half-cycle, and inflow control valve (ICV) for each well at each WAG half-cycle and at each valve. We study different scenarios where we fix some of the design variables to investigate the importance of design variables on life-cycle production optimization of the CO2-WAG problem. We compare the performance of the proposed method using the LS-SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for a synthetic example considering a three-layer, channelized reservoir with 4 injectors and 9 producers. Results show that the proposed LS-SVR-based optimization framework is at least 3 to 6 times computationally more efficient, depending on the cases considered, than the StoSAG using a high-fidelity numerical simulator. However, we observe that the size and sampling of the training data, as well as the selection of well controls and their bound constraints for the well controls, seem to be influential on the performance of the LS-SVR-based optimization method. This is the first LS-SVR application to the CO2-WAG optimal well-control problem. The proposed LS-SVR-based optimization framework has great potential to be used as an efficient tool for the CO2-WAG optimization problem.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122457745","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 drill bit dynamics sensor system has been developed with a new approach that enables economical, widespread use of downhole data to improve bit design. The system emphasizes ease of deployment and minimal manpower requirements for data interpretation. The goal was to develop a system appropriate for deployment on a new scale to the bit industry. The system consists of a small in-bit sensor coupled with an automated software system that provides direct design guidance targeted at drill bit specialists. This paper aims to detail the design considerations used to develop this system and provide an example application of the technology from field testing.
{"title":"Optimizing Drill Bit Design Iteration with a New Sensor System Developed for Large-Scale Deployment and Automated Analysis of Drilling Dysfunctions","authors":"A. Schen, Ryan Graham, Braden Engel","doi":"10.2118/210170-ms","DOIUrl":"https://doi.org/10.2118/210170-ms","url":null,"abstract":"\u0000 A drill bit dynamics sensor system has been developed with a new approach that enables economical, widespread use of downhole data to improve bit design. The system emphasizes ease of deployment and minimal manpower requirements for data interpretation. The goal was to develop a system appropriate for deployment on a new scale to the bit industry. The system consists of a small in-bit sensor coupled with an automated software system that provides direct design guidance targeted at drill bit specialists. This paper aims to detail the design considerations used to develop this system and provide an example application of the technology from field testing.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129324712","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}
Tiberiu Ioan, Nooruldeen Zeyad Essmat, Gianluigi Moroni, J. Sallis
Current oil and gas volatile market environment and the increase focus towards sustainability, it is essential to develop more economically and ecofriendly technologies in oil and gas industry environment. Maintain well integrity is mandatory towards developing oilfields to contain the reservoir fluids within the wellbore, however it becomes more critical in developing new underground gas storage reservoir in Italy. During construction phase of gas production and storage wells, one goal, besides hydraulic isolation of the production casing with cement, is the sand production containment during production cycle of the field. Sand production, even in small quantities, will eventually erode downhole and surface equipment leading to potential catastrophic scenarios of uncontrol reservoir fluid reaching surface. These can have significant health, environmental, and economic impact. Additionally, the impending need for well intervention, along with high re-entry costs, will further weaken revenue margins. In high permeability reservoirs required for underground gas storage projects, the injection and production cycles can lead to stresses applied in nearby wellbore formation which will destabilize the sandstone grains leading to sand production. To mitigate the sand production into the wellbore, a gravel pack operation will support the wellbore, consolidating the space behind the production screens. In this field, a high-risk failure was identified for traditional alpha-beta gravel pack methodology. This could lead to expensive recovery operations for the client and service provider to restore the well and re-perform the gravel pack. To tap different part of the reservoir, one well in particular had to be sidetracked from 9-5/8in casing resulting in a long clay interval being exposed susceptible of instability. It was required to isolate this interval to avoid disturbing the clay interval during gravel pack operations, however, to accommodate the completion, the optimum solution was to use expandable liner. Using this zonal isolation technique to regain well integrity, along with redesign of gravel pack carrier fluid technology led to a successful job securing client position as a reliable field operator. The field operator was committed for high level of safety during operations, starting from design phase through the execution, to achieve long-term well integrity and performance.
{"title":"Risk Mitigation Measures Implemented by Use of Expandable Liner to Prevent Alpha-beta Gravel Pack Failure When Unstable Shale Formation is Exposed","authors":"Tiberiu Ioan, Nooruldeen Zeyad Essmat, Gianluigi Moroni, J. Sallis","doi":"10.2118/210183-ms","DOIUrl":"https://doi.org/10.2118/210183-ms","url":null,"abstract":"\u0000 Current oil and gas volatile market environment and the increase focus towards sustainability, it is essential to develop more economically and ecofriendly technologies in oil and gas industry environment. Maintain well integrity is mandatory towards developing oilfields to contain the reservoir fluids within the wellbore, however it becomes more critical in developing new underground gas storage reservoir in Italy. During construction phase of gas production and storage wells, one goal, besides hydraulic isolation of the production casing with cement, is the sand production containment during production cycle of the field. Sand production, even in small quantities, will eventually erode downhole and surface equipment leading to potential catastrophic scenarios of uncontrol reservoir fluid reaching surface. These can have significant health, environmental, and economic impact. Additionally, the impending need for well intervention, along with high re-entry costs, will further weaken revenue margins.\u0000 In high permeability reservoirs required for underground gas storage projects, the injection and production cycles can lead to stresses applied in nearby wellbore formation which will destabilize the sandstone grains leading to sand production. To mitigate the sand production into the wellbore, a gravel pack operation will support the wellbore, consolidating the space behind the production screens.\u0000 In this field, a high-risk failure was identified for traditional alpha-beta gravel pack methodology. This could lead to expensive recovery operations for the client and service provider to restore the well and re-perform the gravel pack. To tap different part of the reservoir, one well in particular had to be sidetracked from 9-5/8in casing resulting in a long clay interval being exposed susceptible of instability. It was required to isolate this interval to avoid disturbing the clay interval during gravel pack operations, however, to accommodate the completion, the optimum solution was to use expandable liner. Using this zonal isolation technique to regain well integrity, along with redesign of gravel pack carrier fluid technology led to a successful job securing client position as a reliable field operator. The field operator was committed for high level of safety during operations, starting from design phase through the execution, to achieve long-term well integrity and performance.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115373926","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 reliability of the production operations depends not only on the well performance but also on the effectiveness of the surface facilities to transport and separate the produced fluids. In the case of the unconventional reservoirs, the completion treatments placed to stimulate the long horizontal wells require large volumes of proppant and water. During flowback and even later in the life of the well, fractions of the proppant makes its way to the surface and into the separators. Large accumulations of sand reduce the ability of the separators to perform as designed, impacting production, and requiring complete shut-down for cleaning to restore their original capability. The work introduces an intelligent end-to-end workflow integrating computer vision and data analytics to automatically interpret thermographic images, identifying when a production separator needs condition-based maintenance. The new approach leverages infrared thermography pictures taken from hundreds of separators in an unconventional asset and automates a labor-intensive process to make objective maintenance decisions. Contrasted to the manual method, where vessels were taken offline, visually inspected, and cleaned out on time-based maintenance schedules, this work provides an accurate visualization of the sand level using computer vision. The study demonstrates who how integration of digital technologies such as computer vision and data analytics enable optimization of maintenance work. The application showcases the business impact not only through cycle time reduction and effort, by also enables better decision making and optimization of resources.
{"title":"Intelligent Application of Computer Vision and Data Analytics to Optimize the Separators Cleaning for Unconventional Reservoirs","authors":"J. Parizek, A. Popa, Soong Hay Tam","doi":"10.2118/210408-ms","DOIUrl":"https://doi.org/10.2118/210408-ms","url":null,"abstract":"\u0000 The reliability of the production operations depends not only on the well performance but also on the effectiveness of the surface facilities to transport and separate the produced fluids. In the case of the unconventional reservoirs, the completion treatments placed to stimulate the long horizontal wells require large volumes of proppant and water. During flowback and even later in the life of the well, fractions of the proppant makes its way to the surface and into the separators. Large accumulations of sand reduce the ability of the separators to perform as designed, impacting production, and requiring complete shut-down for cleaning to restore their original capability.\u0000 The work introduces an intelligent end-to-end workflow integrating computer vision and data analytics to automatically interpret thermographic images, identifying when a production separator needs condition-based maintenance. The new approach leverages infrared thermography pictures taken from hundreds of separators in an unconventional asset and automates a labor-intensive process to make objective maintenance decisions. Contrasted to the manual method, where vessels were taken offline, visually inspected, and cleaned out on time-based maintenance schedules, this work provides an accurate visualization of the sand level using computer vision.\u0000 The study demonstrates who how integration of digital technologies such as computer vision and data analytics enable optimization of maintenance work. The application showcases the business impact not only through cycle time reduction and effort, by also enables better decision making and optimization of resources.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854505","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 and gas pipeline failure and leakage can seriously damage people's lives and the ecosystem. The prediction of failure pressure for pipelines with damage is one of the most important and challenging tasks faced by industry, which affects the assessment of pipeline safety. Previous studies widely used industrial models or the finite element (FE) method to predict the failure pressure. However, the industrial models may give limited information, and the FE method has much heavy computation burden. In this work, three machine learning models - artificial neural network (ANN), XGBoost (XGB) and CatBoost (CAT) are developed for forecasting the failure pressure of pipelines with defects. Firstly, the simulation results of the FE method are validated by real failure pressure and compared with the calculation results of industrial models (ASME-B31G and DNV). Then 180 pipeline samples including pipeline attributes and defect sizes collected from real in-line inspection data in a pipeline company and the corresponding FE simulation results of failure pressure of these 180 defective pipelines are used for the training and testing of the machine learning models. The results show that the simulation accuracy of the FE method is higher than the calculation accuracy of the industrial models, and the FE simulation results are suitable to be the outputs of machine learning models. The three machine learning methods all provide satisfactory prediction accuracy in failure pressure. Specifically, CAT is the best machine learning method in this study for its lowest relative error (3.11% on average), mean absolute error (0.53), root mean square error (0.78) and highest coefficient of determination (R2) up to 98% in testing. Moreover, the machine learning models present better performance on average relative errors compared to the industrial models. CAT shows higher accuracy than the industrial models and FE simulation on minimum and average relative errors. Finally, the prediction result of CAT is used to discuss the effect of input features on failure pressure of pipelines, which demonstrates that the importance of features follows the order of pipeline thickness > pipeline outside diameter > defect depth > defect length > defect width. Once the above machine learning methods are used in industry, more and more real data will be collected to train a model and make it more accurate. In this way, these methods will provide an efficient way to evaluate the safety of defective pipelines. In addition, the failure pressure of pipeline could be estimated to help operators figure out a pipeline condition and further prioritize the pipelines for maintenance.
{"title":"Failure Pressure Prediction of Defective Pipeline Using Finite Element Method and Machine Learning Models","authors":"Wei Liu, Zhangxin Chen, Yuan Hu","doi":"10.2118/210406-ms","DOIUrl":"https://doi.org/10.2118/210406-ms","url":null,"abstract":"\u0000 Oil and gas pipeline failure and leakage can seriously damage people's lives and the ecosystem. The prediction of failure pressure for pipelines with damage is one of the most important and challenging tasks faced by industry, which affects the assessment of pipeline safety. Previous studies widely used industrial models or the finite element (FE) method to predict the failure pressure. However, the industrial models may give limited information, and the FE method has much heavy computation burden.\u0000 In this work, three machine learning models - artificial neural network (ANN), XGBoost (XGB) and CatBoost (CAT) are developed for forecasting the failure pressure of pipelines with defects. Firstly, the simulation results of the FE method are validated by real failure pressure and compared with the calculation results of industrial models (ASME-B31G and DNV). Then 180 pipeline samples including pipeline attributes and defect sizes collected from real in-line inspection data in a pipeline company and the corresponding FE simulation results of failure pressure of these 180 defective pipelines are used for the training and testing of the machine learning models.\u0000 The results show that the simulation accuracy of the FE method is higher than the calculation accuracy of the industrial models, and the FE simulation results are suitable to be the outputs of machine learning models. The three machine learning methods all provide satisfactory prediction accuracy in failure pressure. Specifically, CAT is the best machine learning method in this study for its lowest relative error (3.11% on average), mean absolute error (0.53), root mean square error (0.78) and highest coefficient of determination (R2) up to 98% in testing. Moreover, the machine learning models present better performance on average relative errors compared to the industrial models. CAT shows higher accuracy than the industrial models and FE simulation on minimum and average relative errors. Finally, the prediction result of CAT is used to discuss the effect of input features on failure pressure of pipelines, which demonstrates that the importance of features follows the order of pipeline thickness > pipeline outside diameter > defect depth > defect length > defect width.\u0000 Once the above machine learning methods are used in industry, more and more real data will be collected to train a model and make it more accurate. In this way, these methods will provide an efficient way to evaluate the safety of defective pipelines. In addition, the failure pressure of pipeline could be estimated to help operators figure out a pipeline condition and further prioritize the pipelines for maintenance.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127463983","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}
Youngbin Shan, B. Zeng, Glyn Roberts, Yaoguang Wu, Jie Song, Dan Zhou
Billions of dollars are invested into hydraulic fracturing every year. However, how to optimize frac design parameters such as stage and cluster spacing, fluid and proppant volume into each stage, number of perforations per cluster and temporary isolation is still not clear. This paper proposes a new method to estimate the volume of proppant penetrating each perforation hole. By aid of high-resolution optical imaging technology, perforation hole size before and after frac treatment can be accurately measured and interpreted. The difference in area before and after Frac is the eroded perforation area. Eroded area represents the sand entry into a perforation. Sand Entry Distribution (SED) determines the frac efficiency. Better SED gives better frac efficiency. Operators understand that different frac treatment volumes and different cluster versus perforation holes designs will impact the productivity. With the technology proposed in this paper, different frac designs can be measured and calculated using SED. A better SED in a cluster and/or a stage is certainly a better frac design. The method described in this paper estimates the proppant volume penetrating into each perforation hole after frac. Furthermore, a number of statistics and comparison between clusters and stages are described in the paper to give operators a clear indication of which frac design gives better frac efficiency and more uniform distribution.
{"title":"Frac Optimization by Sand Entry Distribution","authors":"Youngbin Shan, B. Zeng, Glyn Roberts, Yaoguang Wu, Jie Song, Dan Zhou","doi":"10.2118/209976-ms","DOIUrl":"https://doi.org/10.2118/209976-ms","url":null,"abstract":"\u0000 Billions of dollars are invested into hydraulic fracturing every year. However, how to optimize frac design parameters such as stage and cluster spacing, fluid and proppant volume into each stage, number of perforations per cluster and temporary isolation is still not clear.\u0000 This paper proposes a new method to estimate the volume of proppant penetrating each perforation hole. By aid of high-resolution optical imaging technology, perforation hole size before and after frac treatment can be accurately measured and interpreted. The difference in area before and after Frac is the eroded perforation area. Eroded area represents the sand entry into a perforation. Sand Entry Distribution (SED) determines the frac efficiency. Better SED gives better frac efficiency.\u0000 Operators understand that different frac treatment volumes and different cluster versus perforation holes designs will impact the productivity. With the technology proposed in this paper, different frac designs can be measured and calculated using SED. A better SED in a cluster and/or a stage is certainly a better frac design.\u0000 The method described in this paper estimates the proppant volume penetrating into each perforation hole after frac. Furthermore, a number of statistics and comparison between clusters and stages are described in the paper to give operators a clear indication of which frac design gives better frac efficiency and more uniform distribution.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127508066","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. Hendraningrat, S. Majidaie, N. I. Kechut, R. Tewari, M. Sedaralit, F. Skoreyko, Seyed Mousa Mousavimirkalaei, M. Edmondson, V. Chandrasekar
The deployment at the field-scale of a novel technique to improve oil recovery using nanoparticles injection is challenging. It requires a comprehensive evaluation of a series of laboratory experiments, to translate and validate the mechanisms into a numerical model to predict accurately and reduce uncertainty parameters. This paper describes the application of novel advanced reservoir modeling for nanoparticles from pore-scale to field-scale, using an offshore Malaysian oilfield as a pilot field case. A series of laboratory experiments (fluid-fluid and fluid-rock) and numerical studies: nanofluid formulation, pore-scale studies, validation, and upscaling process into the field-scale model were carried out. The development of nanofluids was formulated to meet key criteria such as compatibility and thermal stability at the intended field condition. Prior to coreflooding tests with native core, a series of experiments to observe mechanisms were carried out. The results of the laboratory experiments were then validated in the 1D coreflooding model. The procedure was continued with observed critical parameters being scaled-up into 3D field-scale model before running the prediction scenarios. The newly developed nanofluids for the intended field performed well in compatibility and thermal stability tests at reservoir temperature. Precipitation and sedimentation were not observed in this solution. The wettability alteration to more water-wet was observed with consistent results through interfacial tension measurements, contact angle measurements, and relative permeability measurements. Coreflooding was performed using native core, and the reduction of residual oil saturation was approximately 25% between pre- and post-nanoflooding. The adsorption of nanofluids was measured to be around 1.12 mg/g of rock. All these results were input into the model and the history match quality index achieved an acceptable match of ~95%. Several critical parameters for the upscaling process were investigated such as reaction rate of particle aggregation, adsorption, and retention factor. During the scale-up process, the velocity of the fluids and pressure drop were conserved because the recovery is sensitive to flooding rate and the viscosity of the fluids are pressure dependent. The field-scale model was run for the intended field location. The potential of using nanoparticles was evaluated and compared to the no further activity scenario giving an additional recovery factor of approximately 1% per year. The developed method of novel robust advanced reservoir modeling for nanoparticles creates a new reference as the first application in the world of novel advanced numerical modeling at field-scale.
{"title":"Application of Novel Advanced Numerical Modeling of Nanoparticles for Improved Oil Recovery: Laboratory- To Field-Scale","authors":"L. Hendraningrat, S. Majidaie, N. I. Kechut, R. Tewari, M. Sedaralit, F. Skoreyko, Seyed Mousa Mousavimirkalaei, M. Edmondson, V. Chandrasekar","doi":"10.2118/210367-ms","DOIUrl":"https://doi.org/10.2118/210367-ms","url":null,"abstract":"\u0000 The deployment at the field-scale of a novel technique to improve oil recovery using nanoparticles injection is challenging. It requires a comprehensive evaluation of a series of laboratory experiments, to translate and validate the mechanisms into a numerical model to predict accurately and reduce uncertainty parameters. This paper describes the application of novel advanced reservoir modeling for nanoparticles from pore-scale to field-scale, using an offshore Malaysian oilfield as a pilot field case.\u0000 A series of laboratory experiments (fluid-fluid and fluid-rock) and numerical studies: nanofluid formulation, pore-scale studies, validation, and upscaling process into the field-scale model were carried out. The development of nanofluids was formulated to meet key criteria such as compatibility and thermal stability at the intended field condition. Prior to coreflooding tests with native core, a series of experiments to observe mechanisms were carried out. The results of the laboratory experiments were then validated in the 1D coreflooding model. The procedure was continued with observed critical parameters being scaled-up into 3D field-scale model before running the prediction scenarios.\u0000 The newly developed nanofluids for the intended field performed well in compatibility and thermal stability tests at reservoir temperature. Precipitation and sedimentation were not observed in this solution. The wettability alteration to more water-wet was observed with consistent results through interfacial tension measurements, contact angle measurements, and relative permeability measurements. Coreflooding was performed using native core, and the reduction of residual oil saturation was approximately 25% between pre- and post-nanoflooding. The adsorption of nanofluids was measured to be around 1.12 mg/g of rock. All these results were input into the model and the history match quality index achieved an acceptable match of ~95%. Several critical parameters for the upscaling process were investigated such as reaction rate of particle aggregation, adsorption, and retention factor. During the scale-up process, the velocity of the fluids and pressure drop were conserved because the recovery is sensitive to flooding rate and the viscosity of the fluids are pressure dependent. The field-scale model was run for the intended field location. The potential of using nanoparticles was evaluated and compared to the no further activity scenario giving an additional recovery factor of approximately 1% per year.\u0000 The developed method of novel robust advanced reservoir modeling for nanoparticles creates a new reference as the first application in the world of novel advanced numerical modeling at field-scale.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121959803","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 the planning, offshore execution and technology involved in the intact salvage, removal, preservation and relocation of a Well Head Drilling Platform (WHP) which was tilted during drilling operation in the "X" field. The field development consists of a WHP tied back to a Floating, Production, Storage & Offloading (FPSO), anchored at 700 m away from the WHP. The oil field is located 110 km from shore and at water depth of 57 m. The Project Management Team (PMT) had completed the installation of the WHP, unfortunately mishap was happened when the WHP experienced tilting during drilling operation. The platform tilted/leaned two (2) degrees towards the drilling rig. The strategy adopted by the PMT was to rig down and move out the affected rig; immediately salvage the newly installed 1,300MT WHP's topside. The work was executed under the crisis management envelop with the aim to save the rig and platform from total loss i.e., to avoid the platform topples into the sea and subsequently hits the rig. The salvage operation employed unique processes, procedures, and technology to safe hold the tilted platform by Anchor Handling Tugs (AHTs) and pipelay barge; rig down and move out the drilling rig, reinstatement of lifting lug/pad eyes which had previously removed after completion of topside installation and finally removal of topside from the tilted jacket. The topside then transported to the fabrication yard, where there the topside had been preserved on the transportation barge for a period of five (5) months while waiting for the new jacket to be fabricated and installed. The re-development of the affected offshore facilities from the incident involved installation of new jacket at hundred fifty (150) meters away from the tilted jacket location, re-installation of the topside to the new installed 4-legged jacket, re-routing the previous installed infield pipelines (8" Liquid, 16" Wet Gas and 12’ Export Gas pipeline from FPSO) and tied-in to the new platform. The planning, innovation and execution has resulted in a significant cost containment and managed to avoid major disaster; subsequently safeguard Company's reputation. The salvage of the topside and rejuvenation of the pipelines have managed to avoid the reconstruction of the topside module which potentially could lead to non-cost recovery of huge amount of additional cost (in USD millions) and managed to avoid any Loss of Primary Containment (LOPC) by taken all the necessary precautions.
{"title":"Salvage of Tilted Wellhead Platform During Drilling Operation; Removal and Relocation of the Wellhead Platform's Topside for Field Re-Development","authors":"S. Zainal Abidin, Helmi Ngadiman, Faizal Shahudin","doi":"10.2118/210046-ms","DOIUrl":"https://doi.org/10.2118/210046-ms","url":null,"abstract":"\u0000 This paper describes the planning, offshore execution and technology involved in the intact salvage, removal, preservation and relocation of a Well Head Drilling Platform (WHP) which was tilted during drilling operation in the \"X\" field.\u0000 The field development consists of a WHP tied back to a Floating, Production, Storage & Offloading (FPSO), anchored at 700 m away from the WHP. The oil field is located 110 km from shore and at water depth of 57 m. The Project Management Team (PMT) had completed the installation of the WHP, unfortunately mishap was happened when the WHP experienced tilting during drilling operation. The platform tilted/leaned two (2) degrees towards the drilling rig.\u0000 The strategy adopted by the PMT was to rig down and move out the affected rig; immediately salvage the newly installed 1,300MT WHP's topside. The work was executed under the crisis management envelop with the aim to save the rig and platform from total loss i.e., to avoid the platform topples into the sea and subsequently hits the rig.\u0000 The salvage operation employed unique processes, procedures, and technology to safe hold the tilted platform by Anchor Handling Tugs (AHTs) and pipelay barge; rig down and move out the drilling rig, reinstatement of lifting lug/pad eyes which had previously removed after completion of topside installation and finally removal of topside from the tilted jacket. The topside then transported to the fabrication yard, where there the topside had been preserved on the transportation barge for a period of five (5) months while waiting for the new jacket to be fabricated and installed. The re-development of the affected offshore facilities from the incident involved installation of new jacket at hundred fifty (150) meters away from the tilted jacket location, re-installation of the topside to the new installed 4-legged jacket, re-routing the previous installed infield pipelines (8\" Liquid, 16\" Wet Gas and 12’ Export Gas pipeline from FPSO) and tied-in to the new platform. The planning, innovation and execution has resulted in a significant cost containment and managed to avoid major disaster; subsequently safeguard Company's reputation. The salvage of the topside and rejuvenation of the pipelines have managed to avoid the reconstruction of the topside module which potentially could lead to non-cost recovery of huge amount of additional cost (in USD millions) and managed to avoid any Loss of Primary Containment (LOPC) by taken all the necessary precautions.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124753882","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}
For almost a decade, the predominant completion drill-out bits utilized to drill composite frac plugs were roller-cone (RC) bits incorporating "hybrid" cutting structures. RC hybrid cutting structures consist of various layouts incorporating a combination of milled teeth (MT) and tungsten carbide insert (TCI) cutting elements that exhibit known trade-offs regarding longevity and performance. The objective of this paper is to illustrate how practicing engineers can, and should, question status quo to overcome traditional design/performance limiters. Extensive analysis of hybrid RC dull bits and performance data was conducted with the goal to advance RC completion drill bit longevity and performance while reducing non-productive time (NPT). Through quantifying and classifying cutting structure damage across 30 RC hybrid drill bits, data collected clearly illustrated which portions of the bit profile and cutting elements were sustaining the most damage. The data indicated commonly accepted hybrid RC designs display an inherent weakness that would require questioning common beliefs about completion RC bit design and manufacturing methodologies. A new bit design was developed and extensively field tested. The results of the dull bit evaluation indicated the MT are inherently less robust and result in more performance limiting cutting structure damage. The MT have been utilized as a standard due to industry acceptance, manufacturing limitations associated with implementing the more robust TCI's in all portions of the bit profile and perceived benefits with MT geometry. Implementing full TCI coverage to mitigate cutting structure damage required rethinking longstanding manufacturing methods and cutting element selection that have been accepted as industry standards. Changes in manufacturing methodology required increasing surface hardness of the cone face around TCI's to avoid loss due to interaction with slip debris and/or weakened TCI retention due to erosion. This change required a substantial and challenging shift in heat-treating methods and manufacturing workflow. Further changes were made to the TCI geometries in the new design to ensure the aggressiveness needed to fail soft composite plug materials into small debris sizes was equivalent or better than the MT cutting elements. The manufacturing, material and geometric changes resulted in a solution that contradicted previous trade-off understandings regarding completion drill bits by simultaneously improving durability and aggressiveness. The work exemplifies the importance for practicing engineers continuously to question status quo in pursuit of continuous improvement even when faced with longstanding beliefs and/or methodologies. Furthermore, the findings from the project give insight into completion drill-out trends and opportunities to reduce NPT and improve efficiency.
{"title":"Innovative Approach to Maximizing Completion Drill Bit Longevity","authors":"Dustin Lyles, Cameron Devers, Warren Dyer, Shawn Lyles","doi":"10.2118/210203-ms","DOIUrl":"https://doi.org/10.2118/210203-ms","url":null,"abstract":"\u0000 For almost a decade, the predominant completion drill-out bits utilized to drill composite frac plugs were roller-cone (RC) bits incorporating \"hybrid\" cutting structures. RC hybrid cutting structures consist of various layouts incorporating a combination of milled teeth (MT) and tungsten carbide insert (TCI) cutting elements that exhibit known trade-offs regarding longevity and performance. The objective of this paper is to illustrate how practicing engineers can, and should, question status quo to overcome traditional design/performance limiters. Extensive analysis of hybrid RC dull bits and performance data was conducted with the goal to advance RC completion drill bit longevity and performance while reducing non-productive time (NPT). Through quantifying and classifying cutting structure damage across 30 RC hybrid drill bits, data collected clearly illustrated which portions of the bit profile and cutting elements were sustaining the most damage. The data indicated commonly accepted hybrid RC designs display an inherent weakness that would require questioning common beliefs about completion RC bit design and manufacturing methodologies. A new bit design was developed and extensively field tested. The results of the dull bit evaluation indicated the MT are inherently less robust and result in more performance limiting cutting structure damage. The MT have been utilized as a standard due to industry acceptance, manufacturing limitations associated with implementing the more robust TCI's in all portions of the bit profile and perceived benefits with MT geometry. Implementing full TCI coverage to mitigate cutting structure damage required rethinking longstanding manufacturing methods and cutting element selection that have been accepted as industry standards. Changes in manufacturing methodology required increasing surface hardness of the cone face around TCI's to avoid loss due to interaction with slip debris and/or weakened TCI retention due to erosion. This change required a substantial and challenging shift in heat-treating methods and manufacturing workflow. Further changes were made to the TCI geometries in the new design to ensure the aggressiveness needed to fail soft composite plug materials into small debris sizes was equivalent or better than the MT cutting elements. The manufacturing, material and geometric changes resulted in a solution that contradicted previous trade-off understandings regarding completion drill bits by simultaneously improving durability and aggressiveness. The work exemplifies the importance for practicing engineers continuously to question status quo in pursuit of continuous improvement even when faced with longstanding beliefs and/or methodologies. Furthermore, the findings from the project give insight into completion drill-out trends and opportunities to reduce NPT and improve efficiency.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123294169","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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