Sergio Garcia, Michael Mendez, Ramadan Ahmed, Hamidreza Karami, Mustafa Nasser, Ibnelwaleed A. Hussein
Summary The deposition of rock cuttings is a problem commonly faced during drilling, completion, and intervention operations. Using polymer-based fluids is a common technique to improve horizontal downhole cleaning. However, these fluids cannot always guarantee an efficient wellbore cleanout. One way to enhance cleanout efficiency is by rotating the drillpipe to mitigate the settling of solids and facilitate their removal. However, drillstring rotation often increases equivalent circulating density (ECD). Therefore, in this study, we explore how the impact of rotation on hole cleaning can be synergized by using fibrous water-based polymeric fluids to perform cleanout at reduced rotational speeds with limited effect on ECD. The flow loop used for this study consists of a 48-ft long eccentric annular (5×2.375 in.) test section. Each experiment began by forming a stationary bed of natural sand (an average diameter of 1.2 mm) in the test section. High-viscosity and low-viscosity polymer-based suspensions with and without fibers were used. The drillpipe rotation speed was varied from 0 to 150 rev/min. In each experiment, the flow rate was increased from 35 to 195 gal/min stepwise. The bed perimeter was measured at equilibrium condition for every test flow rate until a complete bed cleanout was achieved. In addition, the friction pressure loss was measured. Rotational viscometers were also used to measure fluid rheology before and after each test. Fiber particles improve the carrying capacity of the fluid by reducing solid settling and minimizing the redeposition of particles. The results demonstrate the effectiveness of fiber in synergizing pipe rotation effects on hole cleanout performance in horizontal wellbores. Fiber’s impact is more pronounced when used with low-viscosity fluid. The cleanout performance of the low-viscosity fluid is amplified significantly with rotation, almost entirely cleaning the bed at 75 gal/min and a rotational speed of 50 rev/min, compared with more than 195 gal/min without rotation. Even more improvement could be achieved by adding a small amount of fiber (0.04wt%). In addition, the fiber improved the cleanout performance of the high-viscosity fluid. The enhancement, however, was not as noticeable as with the low-viscosity fluid. In general, rotation combined with low-viscosity fibrous fluid exhibits the best cleaning performance. This is because rotating the pipe resuspends the settled solids, which are then easily carried by fibrous fluid that has high solids carrying capacity.
{"title":"Effects of Pipe Rotation on the Performance of Fibrous Water-Based Polymeric Fluids in Horizontal Well Cleanout","authors":"Sergio Garcia, Michael Mendez, Ramadan Ahmed, Hamidreza Karami, Mustafa Nasser, Ibnelwaleed A. Hussein","doi":"10.2118/210347-pa","DOIUrl":"https://doi.org/10.2118/210347-pa","url":null,"abstract":"Summary The deposition of rock cuttings is a problem commonly faced during drilling, completion, and intervention operations. Using polymer-based fluids is a common technique to improve horizontal downhole cleaning. However, these fluids cannot always guarantee an efficient wellbore cleanout. One way to enhance cleanout efficiency is by rotating the drillpipe to mitigate the settling of solids and facilitate their removal. However, drillstring rotation often increases equivalent circulating density (ECD). Therefore, in this study, we explore how the impact of rotation on hole cleaning can be synergized by using fibrous water-based polymeric fluids to perform cleanout at reduced rotational speeds with limited effect on ECD. The flow loop used for this study consists of a 48-ft long eccentric annular (5×2.375 in.) test section. Each experiment began by forming a stationary bed of natural sand (an average diameter of 1.2 mm) in the test section. High-viscosity and low-viscosity polymer-based suspensions with and without fibers were used. The drillpipe rotation speed was varied from 0 to 150 rev/min. In each experiment, the flow rate was increased from 35 to 195 gal/min stepwise. The bed perimeter was measured at equilibrium condition for every test flow rate until a complete bed cleanout was achieved. In addition, the friction pressure loss was measured. Rotational viscometers were also used to measure fluid rheology before and after each test. Fiber particles improve the carrying capacity of the fluid by reducing solid settling and minimizing the redeposition of particles. The results demonstrate the effectiveness of fiber in synergizing pipe rotation effects on hole cleanout performance in horizontal wellbores. Fiber’s impact is more pronounced when used with low-viscosity fluid. The cleanout performance of the low-viscosity fluid is amplified significantly with rotation, almost entirely cleaning the bed at 75 gal/min and a rotational speed of 50 rev/min, compared with more than 195 gal/min without rotation. Even more improvement could be achieved by adding a small amount of fiber (0.04wt%). In addition, the fiber improved the cleanout performance of the high-viscosity fluid. The enhancement, however, was not as noticeable as with the low-viscosity fluid. In general, rotation combined with low-viscosity fibrous fluid exhibits the best cleaning performance. This is because rotating the pipe resuspends the settled solids, which are then easily carried by fibrous fluid that has high solids carrying capacity.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135809613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhong Cheng, Fuqiang Zhang, Liang Zhang, Shuopeng Yang, Jia Wu, Tiantai Li, Ye Liu
In the petroleum and gas industry, optimizing cost-effectiveness remains a paramount objective. One of the key challenges is enhancing predictive models for the rate of penetration (ROP), which are intricately tied to the delicate interplay between significant parameters and drilling efficiency. Recent research has hinted at the potential of temporal and sequential elements in drilling, but a detailed exploration and understanding of these dynamics remain underdeveloped. Addressing this research gap, our primary innovation is not just the introduction of a model but rather the employment of the attention-based long short-term memory (LSTM) network as a tool to deeply analyze the role of sequential features in ROP prediction. Beyond merely applying the model, we furnish a robust foundation for sequential analysis, detailing data processing methods and laying out comprehensive data analytics guidelines for such temporal assessments. The utilization of the LSTM network, in this context, ensures meticulous capture of real-time drilling data nuances, providing insights that are both profound and actionable. Through empirical evaluations with real-world data sets, we accentuate the vital importance of time-sequential dynamics in refining ROP predictions. Our methodological approach, tailored for the oilfield domain, is both rigorous and illuminating, achieving an R2 score of 0.95 and maintaining a relative error under 10%. This effort goes beyond simply proposing a new predictive mechanism. It establishes the centrality of sequential analysis in the drilling process, charting a course for future research and operational optimization in the petroleum and gas sector. We not only offer enhanced modeling strategies but also pioneer insights that can shape the next frontier of industry advancements.
{"title":"A Sequential Feature-Based Rate of Penetration Representation Prediction Method by Attention Long Short-Term Memory Network","authors":"Zhong Cheng, Fuqiang Zhang, Liang Zhang, Shuopeng Yang, Jia Wu, Tiantai Li, Ye Liu","doi":"10.2118/217994-pa","DOIUrl":"https://doi.org/10.2118/217994-pa","url":null,"abstract":"In the petroleum and gas industry, optimizing cost-effectiveness remains a paramount objective. One of the key challenges is enhancing predictive models for the rate of penetration (ROP), which are intricately tied to the delicate interplay between significant parameters and drilling efficiency. Recent research has hinted at the potential of temporal and sequential elements in drilling, but a detailed exploration and understanding of these dynamics remain underdeveloped. Addressing this research gap, our primary innovation is not just the introduction of a model but rather the employment of the attention-based long short-term memory (LSTM) network as a tool to deeply analyze the role of sequential features in ROP prediction. Beyond merely applying the model, we furnish a robust foundation for sequential analysis, detailing data processing methods and laying out comprehensive data analytics guidelines for such temporal assessments. The utilization of the LSTM network, in this context, ensures meticulous capture of real-time drilling data nuances, providing insights that are both profound and actionable. Through empirical evaluations with real-world data sets, we accentuate the vital importance of time-sequential dynamics in refining ROP predictions. Our methodological approach, tailored for the oilfield domain, is both rigorous and illuminating, achieving an R2 score of 0.95 and maintaining a relative error under 10%. This effort goes beyond simply proposing a new predictive mechanism. It establishes the centrality of sequential analysis in the drilling process, charting a course for future research and operational optimization in the petroleum and gas sector. We not only offer enhanced modeling strategies but also pioneer insights that can shape the next frontier of industry advancements.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135850215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the usage of geothermal energy as a zero-emission power resource continues to grow in significance, comprehending the interplay between physical and chemical processes within geothermal reservoirs becomes crucial. In this study, a computationally efficient fluid flow and heat transfer model, combined with a fluid chemistry model, is used to simulate fluid circulation and mineral precipitation in reservoir rock, resulting in changes in rock porosity and permeability. A 2D hybrid approach is employed to solve transient mass and momentum conservation equations, coupled with an analytical solution of the energy equation proposed in the literature for geological formations. A marching algorithm is utilized to calculate velocity and temperature fields in the axial direction within the production zone. Mineral scaling is addressed using the outputs of the hybrid model to perform saturation index (SI) and solution/dissolution computations for qualitative and quantitative mineral precipitation modeling. Multiple criteria are considered to assess the likelihood and intensity of fouling issues. The analysis results are used in an empirical model to estimate rock secondary porosity and permeability changes over a 5-year period of heat extraction. The developed simulator is applied to model a site in the Sabalan geothermal field in Iran, and its initial verification is conducted using data from the same site in the literature. The findings in the study for a sensitivity on fluid circulation rate reveal that increasing water circulation flow rate increases precipitation rate and pumping power required. Furthermore, even minor instances of pore blockage can result in notable reductions in permeability. Consequently, ensuring precise control over pressure and temperature during the production phase becomes progressively crucial for both reservoir integrity and production assurance. The proposed framework provides a promising approach for accurate and efficient simulation of geothermal reservoirs to optimize power generation and minimize environmental impact.
{"title":"Mineral Scaling Impact on Petrophysical Properties of Reservoir Rock in a Geothermal Field Located in Northwestern Iran","authors":"Mohammad Zolfagharroshan, Ehsan Khamehchi","doi":"10.2118/217998-pa","DOIUrl":"https://doi.org/10.2118/217998-pa","url":null,"abstract":"As the usage of geothermal energy as a zero-emission power resource continues to grow in significance, comprehending the interplay between physical and chemical processes within geothermal reservoirs becomes crucial. In this study, a computationally efficient fluid flow and heat transfer model, combined with a fluid chemistry model, is used to simulate fluid circulation and mineral precipitation in reservoir rock, resulting in changes in rock porosity and permeability. A 2D hybrid approach is employed to solve transient mass and momentum conservation equations, coupled with an analytical solution of the energy equation proposed in the literature for geological formations. A marching algorithm is utilized to calculate velocity and temperature fields in the axial direction within the production zone. Mineral scaling is addressed using the outputs of the hybrid model to perform saturation index (SI) and solution/dissolution computations for qualitative and quantitative mineral precipitation modeling. Multiple criteria are considered to assess the likelihood and intensity of fouling issues. The analysis results are used in an empirical model to estimate rock secondary porosity and permeability changes over a 5-year period of heat extraction. The developed simulator is applied to model a site in the Sabalan geothermal field in Iran, and its initial verification is conducted using data from the same site in the literature. The findings in the study for a sensitivity on fluid circulation rate reveal that increasing water circulation flow rate increases precipitation rate and pumping power required. Furthermore, even minor instances of pore blockage can result in notable reductions in permeability. Consequently, ensuring precise control over pressure and temperature during the production phase becomes progressively crucial for both reservoir integrity and production assurance. The proposed framework provides a promising approach for accurate and efficient simulation of geothermal reservoirs to optimize power generation and minimize environmental impact.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136161494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary In well control (production) optimization, the computational cost of conducting a full-physics flow simulation on a 3D, rich grid-based model poses a significant challenge. This challenge is exacerbated in a robust optimization (RO) setting, where flow simulation must be repeated for numerous geological realizations, rendering RO impractical for many field-scale cases. In this paper, we introduce and discuss a new optimization workflow that addresses this issue by providing computational efficiency, i.e., achieving a near-global optimum of the predefined objective function with minimal forward model (flow-simulation) evaluations. In this workflow, referred to as “Bayesian optimization (BO),” the objective function for samples of decision (control) variables is first computed using a proper design experiment. Then, given the samples, a Gaussian process regression (GPR) is trained to mimic the surface of the objective function as a surrogate model. While balancing the dilemma to select the next control variable between high mean, low uncertainty (exploitation) and low mean, high uncertainty (exploration), a new control variable is selected, and flow simulation is run for this new point. Later, the GPR is updated, given the output of the flow simulation. This process continues sequentially until the termination criteria are satisfied. To validate the workflow and obtain a better insight into the detailed steps, we first optimized a 1D problem. The workflow is then implemented for a 3D synthetic reservoir model to perform RO in a realistic field scenario (8-dimensional and 45-dimensional optimization problems). The workflow is compared with two other commonly used gradient-free algorithms in the literature: particle swarm optimization (PSO) and genetic algorithm (GA). The main contributions are (1) developing a new optimization workflow to address the computational cost of flow simulation in RO, (2) demonstrating the effectiveness of the workflow on a 3D grid-based model, (3) investigating the robustness of the workflow against randomness in initiation samples and discussing the results, and (4) comparing the workflow with other optimization algorithms, showing that it achieves same near-optimal results while requiring only a fraction of the computational time.
{"title":"Reservoir Production Management With Bayesian Optimization: Achieving Robust Results in a Fraction of the Time","authors":"Peyman Kor, Aojie Hong, Reidar Bratvold","doi":"10.2118/217985-pa","DOIUrl":"https://doi.org/10.2118/217985-pa","url":null,"abstract":"Summary In well control (production) optimization, the computational cost of conducting a full-physics flow simulation on a 3D, rich grid-based model poses a significant challenge. This challenge is exacerbated in a robust optimization (RO) setting, where flow simulation must be repeated for numerous geological realizations, rendering RO impractical for many field-scale cases. In this paper, we introduce and discuss a new optimization workflow that addresses this issue by providing computational efficiency, i.e., achieving a near-global optimum of the predefined objective function with minimal forward model (flow-simulation) evaluations. In this workflow, referred to as “Bayesian optimization (BO),” the objective function for samples of decision (control) variables is first computed using a proper design experiment. Then, given the samples, a Gaussian process regression (GPR) is trained to mimic the surface of the objective function as a surrogate model. While balancing the dilemma to select the next control variable between high mean, low uncertainty (exploitation) and low mean, high uncertainty (exploration), a new control variable is selected, and flow simulation is run for this new point. Later, the GPR is updated, given the output of the flow simulation. This process continues sequentially until the termination criteria are satisfied. To validate the workflow and obtain a better insight into the detailed steps, we first optimized a 1D problem. The workflow is then implemented for a 3D synthetic reservoir model to perform RO in a realistic field scenario (8-dimensional and 45-dimensional optimization problems). The workflow is compared with two other commonly used gradient-free algorithms in the literature: particle swarm optimization (PSO) and genetic algorithm (GA). The main contributions are (1) developing a new optimization workflow to address the computational cost of flow simulation in RO, (2) demonstrating the effectiveness of the workflow on a 3D grid-based model, (3) investigating the robustness of the workflow against randomness in initiation samples and discussing the results, and (4) comparing the workflow with other optimization algorithms, showing that it achieves same near-optimal results while requiring only a fraction of the computational time.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135663739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary The rate of penetration (ROP) is a critical parameter in drilling operations, essential for optimizing the drilling process and enhancing drilling speed and efficiency. Traditional and statistical models are inadequate for predicting ROP in complex formations, as they fail to conduct a comprehensive analysis of method validity and data validity. In this study, geological conditions parameters, mechanical parameters, and drilling fluid parameters were extracted as prediction parameters, and an intelligent ROP prediction method was constructed under method-data dual validity analysis. The effectiveness of the ROP prediction method is studied by comparing five machine learning algorithms. The data validity of ROP prediction is also studied by changing the input data type, input data dimension, and input data sampling method. The results show that the effectiveness of the long short-term memory (LSTM) neural network method was found to be superior to support vector regression (SVR), backpropagation (BP) neural network, deep belief neural network (DBN), and convolutional neural network (CNN) methods. For data validity, the best input data type for ROP prediction is geological conditions parameters after principal component analysis (PCA) combined with mechanical parameters and drilling fluid parameters. The lower limit of input data dimension validity is seven input parameters, and the accuracy of prediction results increases with the increase of data dimension. The optimal data sampling method is one point per meter, and the error of the prediction result increases and then decreases with the increase of sampling points. Through step-by-step analysis of method validity, input data type, input data dimension, and input data sampling method, the range, size, and mean of error values of ROP prediction results were significantly reduced, and the mean absolute percentage error (MAPE) of the prediction results of the test set is only 18.40%, while the MAPE of the prediction results of the case study is only 11.60%. The results of this study can help to accurately predict ROP, achieve drilling speedup in complex formations, and promote the efficient development of hydrocarbons in the study area.
{"title":"Intelligent Prediction of Drilling Rate of Penetration Based on Method-Data Dual Validity Analysis","authors":"Youwei Wan, Xiangjun Liu, Jian Xiong, Lixi Liang, Yi Ding, Lianlang Hou","doi":"10.2118/217977-pa","DOIUrl":"https://doi.org/10.2118/217977-pa","url":null,"abstract":"Summary The rate of penetration (ROP) is a critical parameter in drilling operations, essential for optimizing the drilling process and enhancing drilling speed and efficiency. Traditional and statistical models are inadequate for predicting ROP in complex formations, as they fail to conduct a comprehensive analysis of method validity and data validity. In this study, geological conditions parameters, mechanical parameters, and drilling fluid parameters were extracted as prediction parameters, and an intelligent ROP prediction method was constructed under method-data dual validity analysis. The effectiveness of the ROP prediction method is studied by comparing five machine learning algorithms. The data validity of ROP prediction is also studied by changing the input data type, input data dimension, and input data sampling method. The results show that the effectiveness of the long short-term memory (LSTM) neural network method was found to be superior to support vector regression (SVR), backpropagation (BP) neural network, deep belief neural network (DBN), and convolutional neural network (CNN) methods. For data validity, the best input data type for ROP prediction is geological conditions parameters after principal component analysis (PCA) combined with mechanical parameters and drilling fluid parameters. The lower limit of input data dimension validity is seven input parameters, and the accuracy of prediction results increases with the increase of data dimension. The optimal data sampling method is one point per meter, and the error of the prediction result increases and then decreases with the increase of sampling points. Through step-by-step analysis of method validity, input data type, input data dimension, and input data sampling method, the range, size, and mean of error values of ROP prediction results were significantly reduced, and the mean absolute percentage error (MAPE) of the prediction results of the test set is only 18.40%, while the MAPE of the prediction results of the case study is only 11.60%. The results of this study can help to accurately predict ROP, achieve drilling speedup in complex formations, and promote the efficient development of hydrocarbons in the study area.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135608353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary Hysteresis of transport properties like relative permeability (kr) can lead to computational problems and inaccuracies for various applications including CO2 sequestration and chemical enhanced oil recovery (EOR). Computational problems in multiphase numerical simulation include phase labeling issues and path dependencies that can create discontinuities. To mitigate hysteresis, modeling kr as a state function that honors changes in physical parameters like wettability is a promising solution. In this research, we apply the state function concept to develop a physics-informed data-driven approach for predicting kr in the space of its state parameters. We extend the development of the relative permeability equation-of-state (kr-EoS) to create a predictive physically-constrained model using artificial neural networks (ANNs). We predict kr as a function of phase saturation (S) and phase connectivity (χ^), as well as the specific S-χ^ path taken during the displacement while maintaining other state parameters constant such as wettability, pore structure, and capillary number. We use numerical data generated from pore-network modeling (PNM) simulations to test the predictive capability of the EoS. Physical limits within S-χ^ space are used to constrain the model and improve its predictability outside of the region of measured data. We find that the predicted relative permeabilities result in a smooth and physically consistent estimate. Our results show that ANN can more accurately estimate kr surface compared to using a high-order polynomial response surface. With only a limited amount of drainage and imbibition data with an initial phase saturation greater than 0.7, we provide a good prediction of kr from ANN for all other initial conditions, over the entire S-χ^ space. Finally, we show that we can predict the specific path taken in the S-χ^ space along with the corresponding kr for any initial condition and flow direction, making the approach practical when phase connectivity information is unavailable. This research demonstrates the first application of a physics-informed data-driven approach for the prediction of relative permeability using ANN.
{"title":"Predictive Model for Relative Permeability Using Physically-Constrained Artificial Neural Networks","authors":"Hanif F. Yoga, Russell T. Johns, Prakash Purswani","doi":"10.2118/209420-pa","DOIUrl":"https://doi.org/10.2118/209420-pa","url":null,"abstract":"Summary Hysteresis of transport properties like relative permeability (kr) can lead to computational problems and inaccuracies for various applications including CO2 sequestration and chemical enhanced oil recovery (EOR). Computational problems in multiphase numerical simulation include phase labeling issues and path dependencies that can create discontinuities. To mitigate hysteresis, modeling kr as a state function that honors changes in physical parameters like wettability is a promising solution. In this research, we apply the state function concept to develop a physics-informed data-driven approach for predicting kr in the space of its state parameters. We extend the development of the relative permeability equation-of-state (kr-EoS) to create a predictive physically-constrained model using artificial neural networks (ANNs). We predict kr as a function of phase saturation (S) and phase connectivity (χ^), as well as the specific S-χ^ path taken during the displacement while maintaining other state parameters constant such as wettability, pore structure, and capillary number. We use numerical data generated from pore-network modeling (PNM) simulations to test the predictive capability of the EoS. Physical limits within S-χ^ space are used to constrain the model and improve its predictability outside of the region of measured data. We find that the predicted relative permeabilities result in a smooth and physically consistent estimate. Our results show that ANN can more accurately estimate kr surface compared to using a high-order polynomial response surface. With only a limited amount of drainage and imbibition data with an initial phase saturation greater than 0.7, we provide a good prediction of kr from ANN for all other initial conditions, over the entire S-χ^ space. Finally, we show that we can predict the specific path taken in the S-χ^ space along with the corresponding kr for any initial condition and flow direction, making the approach practical when phase connectivity information is unavailable. This research demonstrates the first application of a physics-informed data-driven approach for the prediction of relative permeability using ANN.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136161222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary Hydraulic fracturing technology has been widely used to improve the productivity of the coalbed methane (CBM) reservoir, during which tons of fracturing fluids infiltrate the coal seam. However, the effects of fracturing fluids imbibition on CBM recovery are still unclear. In this study, spontaneous and forced water imbibition experiments in methane-bearing low-volatile bituminous (LVB) coal were conducted at various gas adsorption equilibrium pressures, following which methane desorption and diffusion experiments were performed. These experiments simulated the complete process of fracturing fluid imbibition during well shut-in and subsequent methane production upon reopening, which is helpful in understanding the impact of fracturing fluid imbibition on CBM production. The results show that water imbibition displaces adsorbed methane in the coal matrix, and with reservoir pressure increasing, the displaced effect decreases. Furthermore, the forced imbibition (FI) displaces less methane than the spontaneous imbibition (SI) due to water rapidly filling fractures and blocking methane migration out of the matrix in the FI. In the initial stages of gas production following spontaneous or forced water imbibition, the displaced methane diffuses out of the coal at a rapid rate and then slows down. Furthermore, in the case of FI, a significant amount of residual gas remains after desorption and diffusion due to the water blocking effect. However, the water blocking effect has a minimal impact on coal undergoing SI. In terms of desorption and diffusion, this study provides a comprehensive understanding of the effects of fracturing fluids imbibition on recovery of CBM, which is useful for practical shut-in operations following hydraulic fracturing in LVB coal seams.
{"title":"Effects of Fracturing Fluids Imbibition on CBM Recovery: In Terms of Methane Desorption and Diffusion","authors":"Xiaoxiao Sun, Yanbin Yao, Dameng Liu, Ruying Ma, Yongkai Qiu","doi":"10.2118/217983-pa","DOIUrl":"https://doi.org/10.2118/217983-pa","url":null,"abstract":"Summary Hydraulic fracturing technology has been widely used to improve the productivity of the coalbed methane (CBM) reservoir, during which tons of fracturing fluids infiltrate the coal seam. However, the effects of fracturing fluids imbibition on CBM recovery are still unclear. In this study, spontaneous and forced water imbibition experiments in methane-bearing low-volatile bituminous (LVB) coal were conducted at various gas adsorption equilibrium pressures, following which methane desorption and diffusion experiments were performed. These experiments simulated the complete process of fracturing fluid imbibition during well shut-in and subsequent methane production upon reopening, which is helpful in understanding the impact of fracturing fluid imbibition on CBM production. The results show that water imbibition displaces adsorbed methane in the coal matrix, and with reservoir pressure increasing, the displaced effect decreases. Furthermore, the forced imbibition (FI) displaces less methane than the spontaneous imbibition (SI) due to water rapidly filling fractures and blocking methane migration out of the matrix in the FI. In the initial stages of gas production following spontaneous or forced water imbibition, the displaced methane diffuses out of the coal at a rapid rate and then slows down. Furthermore, in the case of FI, a significant amount of residual gas remains after desorption and diffusion due to the water blocking effect. However, the water blocking effect has a minimal impact on coal undergoing SI. In terms of desorption and diffusion, this study provides a comprehensive understanding of the effects of fracturing fluids imbibition on recovery of CBM, which is useful for practical shut-in operations following hydraulic fracturing in LVB coal seams.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134935458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaohua Zhu, Bowen Zhou, Jun Jing, Jiangmiao Shi, Ruyi Qin
Summary Mechanical cutting of tubing plays a vital role in solving the problem of pipe string jams in workover operations of oil wells. To improve the efficiency of downhole cutting operations and save operation costs, it is necessary to optimize the parameters of downhole-cutting operations. However, previous research did not involve related engineering problems. Therefore, in this paper, the equivalent simulation experiment of downhole cutting is conducted based on actual field data. Cutting speed, feed rate, and cutting thickness are used as parameters while cutting power (P), material removal rate (MRR), and tool chip temperature (T) are used as optimization objectives with the trade-offs between the three objectives considered. The full factorial design is used to carry out the experiments and the combination of grey relational analysis (GRA) method and entropy weight method is used to determine the weight of the three objectives. The influence of cutting parameters on the optimization objectives is analyzed, the mathematical model between cutting parameters and a single objective is established, and the adaptive weight particle swarm algorithm is used to optimize the coefficients of this model. The relationship between the multiobjective model and cutting parameters is established using a multiple nonlinear regression model, and the selection of interaction terms is completed using a stepwise regression method. The reliability of the model is also verified. This paper provides a reference for future research on downhole-cutting problems.
{"title":"Modeling and Optimization of Mechanical Cutting of Downhole Tubing","authors":"Xiaohua Zhu, Bowen Zhou, Jun Jing, Jiangmiao Shi, Ruyi Qin","doi":"10.2118/217974-pa","DOIUrl":"https://doi.org/10.2118/217974-pa","url":null,"abstract":"Summary Mechanical cutting of tubing plays a vital role in solving the problem of pipe string jams in workover operations of oil wells. To improve the efficiency of downhole cutting operations and save operation costs, it is necessary to optimize the parameters of downhole-cutting operations. However, previous research did not involve related engineering problems. Therefore, in this paper, the equivalent simulation experiment of downhole cutting is conducted based on actual field data. Cutting speed, feed rate, and cutting thickness are used as parameters while cutting power (P), material removal rate (MRR), and tool chip temperature (T) are used as optimization objectives with the trade-offs between the three objectives considered. The full factorial design is used to carry out the experiments and the combination of grey relational analysis (GRA) method and entropy weight method is used to determine the weight of the three objectives. The influence of cutting parameters on the optimization objectives is analyzed, the mathematical model between cutting parameters and a single objective is established, and the adaptive weight particle swarm algorithm is used to optimize the coefficients of this model. The relationship between the multiobjective model and cutting parameters is established using a multiple nonlinear regression model, and the selection of interaction terms is completed using a stepwise regression method. The reliability of the model is also verified. This paper provides a reference for future research on downhole-cutting problems.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135656218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary This study elucidates the effects of aging and temperature over the performance of one-part “just add water” (JAW) granite-based geopolymers for application in well cementing and well abandonment. Additionally, the investigation delves into the fluid-state and early-age solid-state properties of these geopolymers, with a particular emphasis on their performance after aging. The aging process extended up to 56 days for assessing mechanical properties and up to 28 days for evaluating hydraulic sealability through dedicated tests. The obtained results unveil a nonlinear correlation between the designated temperature and pumping duration. Notably, the issue of fluid loss emerged as a significant concern for these geopolymers. The early-age strength development of the mix design containing zinc demonstrates adherence to industry norms by achieving minimal strength requirements within 24 hours of curing. Zinc plays a pivotal role as a strength enhancer during the initial curing stages of geopolymers, both under ambient conditions and at elevated temperatures (70℃). However, upon extended curing at elevated temperatures, zinc’s impact slightly diminishes compared with the unmodified mix design. After around 30 days of curing, a consecutive reaction occurs in both the unmodified and zinc-modified mix designs. Aging leads to a decline in the material’s hydraulic sealability that was initially established during the early stages of curing.
{"title":"Aging and Temperature Effects on the Performance of Sustainable One-Part Geopolymers Developed for Well-Cementing Applications","authors":"Mohamed Omran, Mahmoud Khalifeh, Maria Paiva","doi":"10.2118/217993-pa","DOIUrl":"https://doi.org/10.2118/217993-pa","url":null,"abstract":"Summary This study elucidates the effects of aging and temperature over the performance of one-part “just add water” (JAW) granite-based geopolymers for application in well cementing and well abandonment. Additionally, the investigation delves into the fluid-state and early-age solid-state properties of these geopolymers, with a particular emphasis on their performance after aging. The aging process extended up to 56 days for assessing mechanical properties and up to 28 days for evaluating hydraulic sealability through dedicated tests. The obtained results unveil a nonlinear correlation between the designated temperature and pumping duration. Notably, the issue of fluid loss emerged as a significant concern for these geopolymers. The early-age strength development of the mix design containing zinc demonstrates adherence to industry norms by achieving minimal strength requirements within 24 hours of curing. Zinc plays a pivotal role as a strength enhancer during the initial curing stages of geopolymers, both under ambient conditions and at elevated temperatures (70℃). However, upon extended curing at elevated temperatures, zinc’s impact slightly diminishes compared with the unmodified mix design. After around 30 days of curing, a consecutive reaction occurs in both the unmodified and zinc-modified mix designs. Aging leads to a decline in the material’s hydraulic sealability that was initially established during the early stages of curing.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136093373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary CO2 injection in subsurface geological formations (e.g., deep saline aquifers) causes pressure perturbations over a large area surrounding the injection well. Observation wells are widely considered in geologic CO2 storage (GCS) projects where the pressure perturbation induced by CO2 injection is measured. In this work, we use analytical and numerical modeling tools along with field data to examine the pressure behavior in GCS projects before and after CO2 arrival at an observation well. Before CO2 arrival, a baseline pressure trend is established which corresponds to single-phase brine flow across the observation well (approximated by the Theis solution). Therefore, analysis of early time pressure data is straightforward, provides the single-phase flow characteristics (mobility and storativity), and helps in establishing a baseline pressure change that can be extended beyond the single-phase flow period at the observation well. Upon CO2 arrival, a departure from this baseline trend is expected. For the pressure to detect the CO2 arrival at an observation well, the departure from baseline pressure behavior must be significant and well above the background noise levels. We use existing analytical models to determine the strength of the expected pressure departure signal from the baseline trend upon CO2 arrival. The strength of the expected pressure departure is found to be directly proportional to the change in the mobility upon CO2 arrival. Larger change in the flow mobility—compared with single-phase brine mobility—results in a stronger pressure departure signal. In addition, the departure is found to be upward (downward) from the baseline pressure trend when the mobility ratio is less (more) than unity. We present a pressure analysis approach through application to synthetic and field data and show the characteristic pressure behavior before and after CO2 arrival. We show that while generally the pressure can be either above or below the expected baseline pressure trend, it would be likely above the baseline upon CO2 arrival. This is because the mobility ratio becomes less than unity after CO2 arrival. We show that depending on the reservoir characteristics, changes in the pressure trend may or may not be sufficient to detect the CO2 arrival.
{"title":"Transient Pressure Interference during CO2 Injection in Saline Aquifers","authors":"Mehdi Zeidouni","doi":"10.2118/217986-pa","DOIUrl":"https://doi.org/10.2118/217986-pa","url":null,"abstract":"Summary CO2 injection in subsurface geological formations (e.g., deep saline aquifers) causes pressure perturbations over a large area surrounding the injection well. Observation wells are widely considered in geologic CO2 storage (GCS) projects where the pressure perturbation induced by CO2 injection is measured. In this work, we use analytical and numerical modeling tools along with field data to examine the pressure behavior in GCS projects before and after CO2 arrival at an observation well. Before CO2 arrival, a baseline pressure trend is established which corresponds to single-phase brine flow across the observation well (approximated by the Theis solution). Therefore, analysis of early time pressure data is straightforward, provides the single-phase flow characteristics (mobility and storativity), and helps in establishing a baseline pressure change that can be extended beyond the single-phase flow period at the observation well. Upon CO2 arrival, a departure from this baseline trend is expected. For the pressure to detect the CO2 arrival at an observation well, the departure from baseline pressure behavior must be significant and well above the background noise levels. We use existing analytical models to determine the strength of the expected pressure departure signal from the baseline trend upon CO2 arrival. The strength of the expected pressure departure is found to be directly proportional to the change in the mobility upon CO2 arrival. Larger change in the flow mobility—compared with single-phase brine mobility—results in a stronger pressure departure signal. In addition, the departure is found to be upward (downward) from the baseline pressure trend when the mobility ratio is less (more) than unity. We present a pressure analysis approach through application to synthetic and field data and show the characteristic pressure behavior before and after CO2 arrival. We show that while generally the pressure can be either above or below the expected baseline pressure trend, it would be likely above the baseline upon CO2 arrival. This is because the mobility ratio becomes less than unity after CO2 arrival. We show that depending on the reservoir characteristics, changes in the pressure trend may or may not be sufficient to detect the CO2 arrival.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135605298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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