Zhihua Wang, Yunfei Xu, Jinling Li, Hankun Wang, Jiajun Hong, Bo Zhou, H. Pu
When wax deposition behavior occurs, gas condensate well suffers from moderate to serve reduction of productivity, even wellbore region blockage. For the operation and maintenance of a gas condensate well production system, a new methodology is needed to understand the wax deposition pattern in the wellbore region and assess the wax prevention under wellbore conditions. This paper establishes a phase envelope relationship in phase-behavior of typical condensate gas flow. The experiments map the potential deposition location in the wellbore region and capture the chemical wax inhibition performance in terms of wax appearance temperature (WAT), wax crystal morphology, and wax inhibiting rate, etc. The fluid component in wells for determining the envelope relationship in phase-behavior was corrected based on the gas-oil ratio of the actual gas condensate well and the carbon number distribution of the produced condensate oil-gas. The cold finger apparatus and dynamic wax inhibition measurement apparatus were designed to test wax deposition characteristics and evaluate chemical wax inhibition performance. The main test unit comprises a fully-closed high-pressure autoclave and cold finger capable of a maximum temperature of 285 °F and a maximum pressure of 16000 psi. The condensate mixtures were sampled from the wellbore region by downhole fluid sampling method. Starting from chemical wax prevention in wellbore flow, the wax crystal-improved wax inhibitor, which was mainly composed of long-chain hydrocarbons and polymers with polar groups, was employed. The temperature difference, intake pressure, stirring rate, and amount of wax inhibitor were controlled in the experiments. The wax content, WAT, and wax crystal structural characteristics of condensate systems showed noticeable differences from well to well. Using the matched component by the simulation, the wellbore temperature and pressure profiles are reliably predicted, and the envelope relationship in phase behavior of condensate gas flow is reasonably determined. Thermal and molecular diffusion are still the main mechanisms for driving wax deposition behavior in wellbore regions. The critical conditions for wax precipitation, wax deposition characteristics, and potential impact of wax deposition pattern are formulated. With the combined wellbore temperature and pressure profiles, the universal relationship schema for identifying deposition location is derived. The wax deposition location obtained from the schema agrees well with what was detected in actual production. Chemical wax prevention is an effective way to inhibit wax deposition. A maximum WAT reduction of 80% and a wax inhibiting rate of 90% could be achieved with the wax crystal improved wax inhibitor at a concentration of 0.25 wt.%. Understanding the wax deposition pattern in the wellbore region is significant for flow assurance and well operation. It provides evidence for wax prevention in wellbore flow and promotes deep condensate
{"title":"Wax Deposition Pattern in Wellbore Region of Deep Condensate Gas Reservoir and Its Prevention: A Combined Experimental and Simulation Study","authors":"Zhihua Wang, Yunfei Xu, Jinling Li, Hankun Wang, Jiajun Hong, Bo Zhou, H. Pu","doi":"10.2118/210338-ms","DOIUrl":"https://doi.org/10.2118/210338-ms","url":null,"abstract":"\u0000 When wax deposition behavior occurs, gas condensate well suffers from moderate to serve reduction of productivity, even wellbore region blockage. For the operation and maintenance of a gas condensate well production system, a new methodology is needed to understand the wax deposition pattern in the wellbore region and assess the wax prevention under wellbore conditions. This paper establishes a phase envelope relationship in phase-behavior of typical condensate gas flow. The experiments map the potential deposition location in the wellbore region and capture the chemical wax inhibition performance in terms of wax appearance temperature (WAT), wax crystal morphology, and wax inhibiting rate, etc. The fluid component in wells for determining the envelope relationship in phase-behavior was corrected based on the gas-oil ratio of the actual gas condensate well and the carbon number distribution of the produced condensate oil-gas. The cold finger apparatus and dynamic wax inhibition measurement apparatus were designed to test wax deposition characteristics and evaluate chemical wax inhibition performance. The main test unit comprises a fully-closed high-pressure autoclave and cold finger capable of a maximum temperature of 285 °F and a maximum pressure of 16000 psi. The condensate mixtures were sampled from the wellbore region by downhole fluid sampling method. Starting from chemical wax prevention in wellbore flow, the wax crystal-improved wax inhibitor, which was mainly composed of long-chain hydrocarbons and polymers with polar groups, was employed. The temperature difference, intake pressure, stirring rate, and amount of wax inhibitor were controlled in the experiments. The wax content, WAT, and wax crystal structural characteristics of condensate systems showed noticeable differences from well to well. Using the matched component by the simulation, the wellbore temperature and pressure profiles are reliably predicted, and the envelope relationship in phase behavior of condensate gas flow is reasonably determined. Thermal and molecular diffusion are still the main mechanisms for driving wax deposition behavior in wellbore regions. The critical conditions for wax precipitation, wax deposition characteristics, and potential impact of wax deposition pattern are formulated. With the combined wellbore temperature and pressure profiles, the universal relationship schema for identifying deposition location is derived. The wax deposition location obtained from the schema agrees well with what was detected in actual production. Chemical wax prevention is an effective way to inhibit wax deposition. A maximum WAT reduction of 80% and a wax inhibiting rate of 90% could be achieved with the wax crystal improved wax inhibitor at a concentration of 0.25 wt.%. Understanding the wax deposition pattern in the wellbore region is significant for flow assurance and well operation. It provides evidence for wax prevention in wellbore flow and promotes deep condensate ","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"12 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":"116792605","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}
Javier Canon, Theresa Broussard, A. Johnson, W. Singletary, Lolymar Colmenares-Diaz
This paper details experiences gained while developing a novel technology-driven approach to Risk Assessment methodologies, e.g., Process Hazard Analysis (PHA), Hazard Identification (HAZID) and Hazard Operability (HAZOP), in oil & gas. Emphasis has been placed on combining encoded human knowledge with Artificial Intelligence techniques, in a way which fosters safer designs and operations, while maintaining Subject Matter Experts (SMEs) at the center of decision making. Encoding of human knowledge (e.g., Subject Matter Expertise, Industry best practices) in digital applications has traditionally been associated with creating static pieces of information, such as lessons learned documentation and validation activities for hazard analysis. New digital technologies, however, make it possible to create truly dynamic knowledge representations, which capture key concepts and their relationships, creating a new type of "source of truth." As a result, corporate and external knowledge can be made more readily accessible to engineers and operations personnel participating in decision making. Digital corporate knowledge can also be supplemented with Artificial Intelligence (AI) techniques which can help uncover latent threats and better guide optimal decision making. This is particularly relevant in Workforce, Health & Safety (WH&S) and Process Safety contexts, where the impact of flawed or suboptimal decisions can lead to catastrophic consequences. Practical examples from an oil & gas major show how the risk assessment domain can be represented in a computational knowledge graph, in a format which is comprehensible not only to software developers, but more importantly, to oil & gas SMEs. A presentation of different AI techniques overlaid on top of this computational knowledge graph, can also offer a glimpse of the possibilities of marrying SME expertise with emerging digital technologies.
{"title":"A Knowledge-Based Artificial Intelligence Approach to Risk Management","authors":"Javier Canon, Theresa Broussard, A. Johnson, W. Singletary, Lolymar Colmenares-Diaz","doi":"10.2118/210303-ms","DOIUrl":"https://doi.org/10.2118/210303-ms","url":null,"abstract":"\u0000 This paper details experiences gained while developing a novel technology-driven approach to Risk Assessment methodologies, e.g., Process Hazard Analysis (PHA), Hazard Identification (HAZID) and Hazard Operability (HAZOP), in oil & gas. Emphasis has been placed on combining encoded human knowledge with Artificial Intelligence techniques, in a way which fosters safer designs and operations, while maintaining Subject Matter Experts (SMEs) at the center of decision making.\u0000 Encoding of human knowledge (e.g., Subject Matter Expertise, Industry best practices) in digital applications has traditionally been associated with creating static pieces of information, such as lessons learned documentation and validation activities for hazard analysis. New digital technologies, however, make it possible to create truly dynamic knowledge representations, which capture key concepts and their relationships, creating a new type of \"source of truth.\" As a result, corporate and external knowledge can be made more readily accessible to engineers and operations personnel participating in decision making.\u0000 Digital corporate knowledge can also be supplemented with Artificial Intelligence (AI) techniques which can help uncover latent threats and better guide optimal decision making. This is particularly relevant in Workforce, Health & Safety (WH&S) and Process Safety contexts, where the impact of flawed or suboptimal decisions can lead to catastrophic consequences.\u0000 Practical examples from an oil & gas major show how the risk assessment domain can be represented in a computational knowledge graph, in a format which is comprehensible not only to software developers, but more importantly, to oil & gas SMEs. A presentation of different AI techniques overlaid on top of this computational knowledge graph, can also offer a glimpse of the possibilities of marrying SME expertise with emerging digital technologies.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"221 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":"116287652","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}
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}
A design optimized coated sand screen was developed that increases the erosion resistance of conventional sand screens. Computational fluid dynamics and laboratory testing were conducted to determine the design optimizations and a coating was developed through rigorous testing. The screen has now been deployed and adopted with success.
{"title":"Erosion Resistant Sand Screen: Development and Deployment","authors":"Antonio Lazo, Jeremy Davis, J. Weirich","doi":"10.2118/210128-ms","DOIUrl":"https://doi.org/10.2118/210128-ms","url":null,"abstract":"\u0000 A design optimized coated sand screen was developed that increases the erosion resistance of conventional sand screens. Computational fluid dynamics and laboratory testing were conducted to determine the design optimizations and a coating was developed through rigorous testing. The screen has now been deployed and adopted with success.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"2 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":"129469122","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}
M. Sviridov, D. Kushnir, A. Mosin, Andrey Belousov, D. Nemushchenko, A. Zaputlyaeva
Logging-while-drilling (LWD) ultra-deep resistivity technology can explore the reservoir on a similar scale to seismic, so interpreted resistivity models can be combined with seismic sections to enable oil field operators to delineate pay zones better, improve reservoir understanding, and eventually achieve higher reservoir contact value by proactive geosteering. Currently, there is no industry-adopted processing software which supports different ultra-deep tools. This paper presents the first vendor-independent, gradient-based stochastic approach for ultra-deep data inversion while drilling. Industry literature review was performed to determine parameters of ultra-deep tools, investigate their responses, and add them to the list of supported devices. Inversion algorithm is based on stochastic Monte Carlo method with reversible jump Markov chains and can be launched automatically without prior assumptions about the reservoir structure. Finally, it provides an ensemble of unbiased 1D formation models explaining the measurements as well as uncertainty estimates of model parameters. Parallel running of several Markov chains on multiple CPUs with both gradient-based sampling and exchanging their states makes the algorithm computationally effective and helps to avoid sticking in local optima. The proposed approach enables gathering of ultra-deep tools from different vendors under a common interface, along with other resistivity tools, joint processing various resistivity data with the same inversion workflow, and representation of inversion deliverables in unified format. Due to the large formation volume being investigated, the ultra-deep readings become complex. To be interpreted, such responses require multi-layer models as well as special multi-parametric inversion software. Working in high-dimensional parameter space, stochastic Monte Carlo inversion algorithms might not be effective due to the limitation of sampling procedure that usually generates new samples through the random perturbation of the few model parameters and does not consider their relations with other model parameters. This may lead to a high rate of proposal rejections and a lot of unnecessary calculations. To overcome this issue and guarantee real-time results, the presented approach employs Metropolis-adjusted Langevin technique which evaluates the gradient of posterior probability density function and generates proposals with a higher posterior probability of being accepted. Additionally, a special fast semi analytical solver is utilized to compute the gradient simultaneously with tool responses, with almost no extra computational costs. Application of the developed software is shown on synthetic scenarios and case studies from Norwegian natural gas and oil fields. The presented approach is identified as the first vendor-independent gradient-based inversion algorithm operating with any measurements of ultra-deep and deep azimuthal resistivity tools available on the ma
{"title":"Reservoir Mapping with Vendor-Independent Gradient-Based Stochastic Inversion of LWD Ultra-Deep Azimuthal Resistivity Data","authors":"M. Sviridov, D. Kushnir, A. Mosin, Andrey Belousov, D. Nemushchenko, A. Zaputlyaeva","doi":"10.2118/210062-ms","DOIUrl":"https://doi.org/10.2118/210062-ms","url":null,"abstract":"\u0000 Logging-while-drilling (LWD) ultra-deep resistivity technology can explore the reservoir on a similar scale to seismic, so interpreted resistivity models can be combined with seismic sections to enable oil field operators to delineate pay zones better, improve reservoir understanding, and eventually achieve higher reservoir contact value by proactive geosteering. Currently, there is no industry-adopted processing software which supports different ultra-deep tools. This paper presents the first vendor-independent, gradient-based stochastic approach for ultra-deep data inversion while drilling.\u0000 Industry literature review was performed to determine parameters of ultra-deep tools, investigate their responses, and add them to the list of supported devices. Inversion algorithm is based on stochastic Monte Carlo method with reversible jump Markov chains and can be launched automatically without prior assumptions about the reservoir structure. Finally, it provides an ensemble of unbiased 1D formation models explaining the measurements as well as uncertainty estimates of model parameters. Parallel running of several Markov chains on multiple CPUs with both gradient-based sampling and exchanging their states makes the algorithm computationally effective and helps to avoid sticking in local optima.\u0000 The proposed approach enables gathering of ultra-deep tools from different vendors under a common interface, along with other resistivity tools, joint processing various resistivity data with the same inversion workflow, and representation of inversion deliverables in unified format.\u0000 Due to the large formation volume being investigated, the ultra-deep readings become complex. To be interpreted, such responses require multi-layer models as well as special multi-parametric inversion software. Working in high-dimensional parameter space, stochastic Monte Carlo inversion algorithms might not be effective due to the limitation of sampling procedure that usually generates new samples through the random perturbation of the few model parameters and does not consider their relations with other model parameters. This may lead to a high rate of proposal rejections and a lot of unnecessary calculations.\u0000 To overcome this issue and guarantee real-time results, the presented approach employs Metropolis-adjusted Langevin technique which evaluates the gradient of posterior probability density function and generates proposals with a higher posterior probability of being accepted. Additionally, a special fast semi analytical solver is utilized to compute the gradient simultaneously with tool responses, with almost no extra computational costs.\u0000 Application of the developed software is shown on synthetic scenarios and case studies from Norwegian natural gas and oil fields.\u0000 The presented approach is identified as the first vendor-independent gradient-based inversion algorithm operating with any measurements of ultra-deep and deep azimuthal resistivity tools available on the ma","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"365 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":"122833648","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}
Abdulrahman A. Almulhim, AbdulMuqtadir Khan, Jon E. Hansen, Hashem Alobaid, D. Emelyanov
The design of fracture diversion in tight carbonates has been a challenging problem. Recently, a conceptual and theoretical workflow was presented using a β diversion design parameter that uses system volumetric calculations based on high-fidelity modeling and mathematical approximations of the etched system. A robust field validation of that approach and near-wellbore diversion modeling was conducted to extend the application. Extensive laboratory and yard-scale testing data were utilized to realize the diversion processes. Fracture and perforation modeling coupled with fracture diagnostics was used to define system volumetrics, defined as the volume where the fluid needs to be diverted away from. Multimodal particulate pills were used based on a careful review of the size distribution and physical properties. Bottomhole reactions and post-fracturing production for multiple wells and 100 particulate pills were studied to see the effect of the β factor on diversion and production performance. A multiphysics near-wellbore diversion model was used for the first time to simulate the pill effect. Representative wells were selected for the validation study; these included vertical and horizontal wells and varying perforation cluster design, stages, and acid treatments. A complex problem was solved with reaction modeling coupled with near-wellbore diversion for the first time based on given lithology and pumped volumes to match the treatment and diversion differential pressures. Final active fractures and stimulation efficiency were computed through etched geometry. The results showed a range of etched fracture length from 86 to 109 ft and width of 0.05 to 0.08 in. A similar approach was used for perforation system analysis. Diversion pills from 2 to 15 per well were investigated with a 5- to 12-bbl particulate diversion pill range. Finally, the β factor was calculated for each case based on the diversion material and system volumetric ratio. The parameter was plotted against the average diversion pressure achieved and showed an R2 of 0.87. Based on the comprehensive theoretical, numerical modeling, and field-coupled findings, a β factor of 0.8 to 1.0 is recommended for optimum diversion and production performance. For multiple cases, stimulation efficiency and production performance have been enhanced up to 200%. From the field results, it is evident that the design of near-wellbore diversion needs to be strategic. The unique diversion framework provides the basis for such a well- and reservoir-specific strategy. Proper and scientific use of diversion material and modeling can lead to advances in overall project management by optimizing the cost–efficiency–quality project triangle. Digital advancements with digitized cores, fluid systems, and advanced modeling have significant potential for the engineered development of tight carbonates.
{"title":"Validation of a Novel Beta Diversion Design Factor for Enhancing Stimulation Efficiency Through Field Cases and Near Wellbore Diversion Model","authors":"Abdulrahman A. Almulhim, AbdulMuqtadir Khan, Jon E. Hansen, Hashem Alobaid, D. Emelyanov","doi":"10.2118/210439-ms","DOIUrl":"https://doi.org/10.2118/210439-ms","url":null,"abstract":"\u0000 The design of fracture diversion in tight carbonates has been a challenging problem. Recently, a conceptual and theoretical workflow was presented using a β diversion design parameter that uses system volumetric calculations based on high-fidelity modeling and mathematical approximations of the etched system. A robust field validation of that approach and near-wellbore diversion modeling was conducted to extend the application.\u0000 Extensive laboratory and yard-scale testing data were utilized to realize the diversion processes. Fracture and perforation modeling coupled with fracture diagnostics was used to define system volumetrics, defined as the volume where the fluid needs to be diverted away from. Multimodal particulate pills were used based on a careful review of the size distribution and physical properties. Bottomhole reactions and post-fracturing production for multiple wells and 100 particulate pills were studied to see the effect of the β factor on diversion and production performance. A multiphysics near-wellbore diversion model was used for the first time to simulate the pill effect.\u0000 Representative wells were selected for the validation study; these included vertical and horizontal wells and varying perforation cluster design, stages, and acid treatments. A complex problem was solved with reaction modeling coupled with near-wellbore diversion for the first time based on given lithology and pumped volumes to match the treatment and diversion differential pressures. Final active fractures and stimulation efficiency were computed through etched geometry. The results showed a range of etched fracture length from 86 to 109 ft and width of 0.05 to 0.08 in. A similar approach was used for perforation system analysis. Diversion pills from 2 to 15 per well were investigated with a 5- to 12-bbl particulate diversion pill range. Finally, the β factor was calculated for each case based on the diversion material and system volumetric ratio. The parameter was plotted against the average diversion pressure achieved and showed an R2 of 0.87. Based on the comprehensive theoretical, numerical modeling, and field-coupled findings, a β factor of 0.8 to 1.0 is recommended for optimum diversion and production performance. For multiple cases, stimulation efficiency and production performance have been enhanced up to 200%.\u0000 From the field results, it is evident that the design of near-wellbore diversion needs to be strategic. The unique diversion framework provides the basis for such a well- and reservoir-specific strategy. Proper and scientific use of diversion material and modeling can lead to advances in overall project management by optimizing the cost–efficiency–quality project triangle. Digital advancements with digitized cores, fluid systems, and advanced modeling have significant potential for the engineered development of tight carbonates.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"108 5 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":"126075206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed Adel Gabry, I. Eltaleb, M. Soliman, S. Farouq Ali
The Diagnostic Fracture Injection Test (DFIT) is widely used to get the fracture closure pressure, reservoir permeability, and reservoir pressure. Conventional methods for analyzing DFIT are based on the assumption of a vertical well but fail for horizontal wells drilled in ultra-low permeability reservoirs with potential multiple closures. There is still a significant debate about the rigorousness and validity of these techniques due to the complexity of the hydraulic fracture opening and closure process and assumptions of conventional fracture detection methods. In this study, a new signal processing approach was proposed by M.Y. Soliman, U. Ebru, F. Siddiqi, A.Rezaei, and I. Eltaleb (2019) and (2020) was extended to use the continuous wavelet transform to identify the closure time and pressure. The new method was applied to synthetic and actual field data. The synthetic data were produced using commercial fracture simulators based on fracture propagation and closure simulation principles with predefined fracture closure. To determine this closure instant, we decompose the pressure fall-off signal as the output of the fracture system into multiple levels with different frequencies using the continuous wavelet transform. This "short wavy" function is stretched or compressed and placed at many positions along the signal to be analyzed. The wavelet is then multiplied term-by-term by the signal, and each product yields a wavelet coefficient value. The signal energy is observed during the fracture closure process (pressure fall-off) and the fracture closure event is identified when the signal energy stabilizes to a minimum level. Because of the uncertainty of the real field fracture closure, a predefined simple synthetic fracture simulation with known fracture closure was used to validate the new methodology. The new continuous wavelet transform technique showed clear success without any prior assumptions or the need for additional reservoir data. The new methodology is also extended to actual field cases and showed the same success as conventional classical methods.
诊断性裂缝注入试验(DFIT)被广泛用于测量裂缝闭合压力、储层渗透率和储层压力。传统的DFIT分析方法是基于直井的假设，但不适用于可能多次闭井的超低渗透油藏中的水平井。由于水力裂缝开启和关闭过程的复杂性以及传统裂缝检测方法的假设，这些技术的严谨性和有效性仍然存在很大的争议。在本研究中，M.Y. Soliman, U. Ebru, F. Siddiqi, a . rezaei和I. Eltaleb(2019)提出了一种新的信号处理方法，并扩展了(2020)使用连续小波变换来识别关闭时间和压力。将新方法应用于综合资料和实际现场资料。基于裂缝扩展和闭合模拟原理，使用商用裂缝模拟器生成合成数据，并预置裂缝闭合。为了确定该闭合时刻，我们利用连续小波变换将作为裂缝系统输出的压力下降信号分解成不同频率的多级信号。这个“短波”函数被拉伸或压缩，并放置在待分析信号的许多位置。然后将小波逐项与信号相乘，每个乘积产生一个小波系数值。在裂缝闭合过程(压力下降)中观察信号能量，当信号能量稳定到最低水平时识别裂缝闭合事件。由于实际现场裂缝闭合的不确定性，采用预先定义的、已知裂缝闭合的简单合成裂缝模拟来验证新方法。新的连续小波变换技术在没有任何预先假设或需要额外油藏数据的情况下取得了明显的成功。新方法也被推广到实际的现场案例中，并取得了与传统经典方法相同的成功。
{"title":"Novel Method to Detect Fracture Closure Event Using Continuous Wavelet Transform","authors":"Mohamed Adel Gabry, I. Eltaleb, M. Soliman, S. Farouq Ali","doi":"10.2118/210267-ms","DOIUrl":"https://doi.org/10.2118/210267-ms","url":null,"abstract":"\u0000 The Diagnostic Fracture Injection Test (DFIT) is widely used to get the fracture closure pressure, reservoir permeability, and reservoir pressure. Conventional methods for analyzing DFIT are based on the assumption of a vertical well but fail for horizontal wells drilled in ultra-low permeability reservoirs with potential multiple closures. There is still a significant debate about the rigorousness and validity of these techniques due to the complexity of the hydraulic fracture opening and closure process and assumptions of conventional fracture detection methods.\u0000 In this study, a new signal processing approach was proposed by M.Y. Soliman, U. Ebru, F. Siddiqi, A.Rezaei, and I. Eltaleb (2019) and (2020) was extended to use the continuous wavelet transform to identify the closure time and pressure. The new method was applied to synthetic and actual field data. The synthetic data were produced using commercial fracture simulators based on fracture propagation and closure simulation principles with predefined fracture closure. To determine this closure instant, we decompose the pressure fall-off signal as the output of the fracture system into multiple levels with different frequencies using the continuous wavelet transform. This \"short wavy\" function is stretched or compressed and placed at many positions along the signal to be analyzed. The wavelet is then multiplied term-by-term by the signal, and each product yields a wavelet coefficient value. The signal energy is observed during the fracture closure process (pressure fall-off) and the fracture closure event is identified when the signal energy stabilizes to a minimum level.\u0000 Because of the uncertainty of the real field fracture closure, a predefined simple synthetic fracture simulation with known fracture closure was used to validate the new methodology. The new continuous wavelet transform technique showed clear success without any prior assumptions or the need for additional reservoir data. The new methodology is also extended to actual field cases and showed the same success as conventional classical methods.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"4 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":"114458128","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}
Dan Fu, W. Zemlak, Tony Yeung, Caleb Barclay, Trevor Gorchynski
Recently, the North America Oil and Gas industry has seen a rapid increase in the adoption of new hydraulic fracturing technologies such as dual-fuel diesel engine, electric system powered by gas turbine or engine on-site and turbine direct drive technology, to reduce emissions and operating costs. The objective of this paper is to provide a detailed analysis of economic, environmental, and technical considerations when selecting the next generation hydraulic fracturing equipment platform. It is believed that any next-generation technology must meet the following key requirements: 1. Reduction of GHG and EPA regulated emissions; 2. Reduced equipment footprint; 3. Capable of meeting the most stringent noise standard; 4. Improved reliability; 5. Improved pad-to-pad mobility; 6. Reduced maintenance and personnel costs; 7. Competitive capital cost. For the selection process, a methodology was developed to evaluate the energy density of fuel, thermal efficiency of prime movers, mechanical power transfer efficiency, and equipment operating environment and configuration against the above objectives. The methodology also considered the technical and commercial feasibility of key components. Natural gas is selected as the mobile primary energy source due to its higher energy density and lower emission profile than conventional diesel, and more economical and widely available on-site. Among all available natural gas-powered engines evaluated, which included dual-fuel diesel engine, gas reciprocating engine, single large turbine and direct drive turbine, the direct drive turbine scored the highest. The direct drive pumping unit is equipped with a 5,000 HHP continuous duty power end driven by a 5,000 HHP dual shaft turbine through a single speed reduction gearbox. This combination provides the most efficient mechanical power transfer efficiency resulting in significant fuel cost savings and reduction in greenhouse gas emissions. Because of its high-power density, the direct drive turbine system can potentially reduce the number of on-site equipment by 43% and personnel by 31%. Comparing to other next generation hydraulic fracturing system, the direct drive turbine technology has the lowest capital cost per HHP.
{"title":"Technical, Economic and Environmental Considerations for Selecting Next Generation Hydraulic Fracturing Equipment Technology","authors":"Dan Fu, W. Zemlak, Tony Yeung, Caleb Barclay, Trevor Gorchynski","doi":"10.2118/210215-ms","DOIUrl":"https://doi.org/10.2118/210215-ms","url":null,"abstract":"\u0000 Recently, the North America Oil and Gas industry has seen a rapid increase in the adoption of new hydraulic fracturing technologies such as dual-fuel diesel engine, electric system powered by gas turbine or engine on-site and turbine direct drive technology, to reduce emissions and operating costs. The objective of this paper is to provide a detailed analysis of economic, environmental, and technical considerations when selecting the next generation hydraulic fracturing equipment platform.\u0000 It is believed that any next-generation technology must meet the following key requirements: 1. Reduction of GHG and EPA regulated emissions; 2. Reduced equipment footprint; 3. Capable of meeting the most stringent noise standard; 4. Improved reliability; 5. Improved pad-to-pad mobility; 6. Reduced maintenance and personnel costs; 7. Competitive capital cost. For the selection process, a methodology was developed to evaluate the energy density of fuel, thermal efficiency of prime movers, mechanical power transfer efficiency, and equipment operating environment and configuration against the above objectives. The methodology also considered the technical and commercial feasibility of key components.\u0000 Natural gas is selected as the mobile primary energy source due to its higher energy density and lower emission profile than conventional diesel, and more economical and widely available on-site. Among all available natural gas-powered engines evaluated, which included dual-fuel diesel engine, gas reciprocating engine, single large turbine and direct drive turbine, the direct drive turbine scored the highest. The direct drive pumping unit is equipped with a 5,000 HHP continuous duty power end driven by a 5,000 HHP dual shaft turbine through a single speed reduction gearbox. This combination provides the most efficient mechanical power transfer efficiency resulting in significant fuel cost savings and reduction in greenhouse gas emissions. Because of its high-power density, the direct drive turbine system can potentially reduce the number of on-site equipment by 43% and personnel by 31%. Comparing to other next generation hydraulic fracturing system, the direct drive turbine technology has the lowest capital cost per HHP.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"16 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120873507","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}
Miguel Gonzalez, Tim Thiel, Nathan St. Michel, J. Harrist, E. Buzi, H. Seren, S. Ayirala, Lyla Maskeen, A. Sofi
Polymer degradation during Enhanced Oil Recovery (EOR) can have large impact on recovery rates during polymer flooding. In the field, few practical solutions exist to perform quality control/assurance (QA/QC) on EOR polymer fluids at surface and no solutions exist for measurements downhole. Here, we present the development of a miniaturized sensor that can be used to detect the onset of polymer degradation by measuring the viscous properties of EOR polymer fluids. The device was tested on samples collected from a polymer flooding operation. We describe its integration into wellsite portable systems and into an untethered logging tool for cost-effective routine measurements downhole. The sensors are based on millimeter-sized piezoelectric tuning fork resonators. The viscosity and density of the fluids was measured from the energy dissipation and the resonant frequency obtained from their vibrational spectra. The devices were specially designed for use in high-salinity polymer fluids. They were tested and validated on samples collected from a single well polymer flood trial. A miniaturized electrical measurement platform was then designed for use at surface in the field and for use in a compact untethered logging tool for quick and inexpensive deployment downhole. The devices were initially calibrated in the laboratory and then tested on samples collected from the field. These two field-collected solutions were used to preflush the formation before injecting surfactant-polymer solution and as a polymer taper to drive the injected surfactant-polymer solution, respectively. The obtained viscosity values correlated very well with those obtained from standard laboratory measurements. Therefore, the changes in viscosity due to reduction in the molecular weight of the polymer, as measured with the miniature devices, can be used to assess whether degradation has taken place. A miniaturized electrical measurement platform was then tested in comparable polymer fluids for use in the field and obtained comparable results. The platforms described here provide a simple, cost-effective, and user-friendly platform for the detection of polymer degradation in the field, thus providing valuable information in real-time during costly polymer flooding operations.
{"title":"A New Viscosity Sensing Platform for the Assessment of Polymer Degradation in EOR Polymer Fluids","authors":"Miguel Gonzalez, Tim Thiel, Nathan St. Michel, J. Harrist, E. Buzi, H. Seren, S. Ayirala, Lyla Maskeen, A. Sofi","doi":"10.2118/210014-ms","DOIUrl":"https://doi.org/10.2118/210014-ms","url":null,"abstract":"\u0000 Polymer degradation during Enhanced Oil Recovery (EOR) can have large impact on recovery rates during polymer flooding. In the field, few practical solutions exist to perform quality control/assurance (QA/QC) on EOR polymer fluids at surface and no solutions exist for measurements downhole. Here, we present the development of a miniaturized sensor that can be used to detect the onset of polymer degradation by measuring the viscous properties of EOR polymer fluids. The device was tested on samples collected from a polymer flooding operation. We describe its integration into wellsite portable systems and into an untethered logging tool for cost-effective routine measurements downhole. The sensors are based on millimeter-sized piezoelectric tuning fork resonators. The viscosity and density of the fluids was measured from the energy dissipation and the resonant frequency obtained from their vibrational spectra. The devices were specially designed for use in high-salinity polymer fluids. They were tested and validated on samples collected from a single well polymer flood trial. A miniaturized electrical measurement platform was then designed for use at surface in the field and for use in a compact untethered logging tool for quick and inexpensive deployment downhole. The devices were initially calibrated in the laboratory and then tested on samples collected from the field. These two field-collected solutions were used to preflush the formation before injecting surfactant-polymer solution and as a polymer taper to drive the injected surfactant-polymer solution, respectively. The obtained viscosity values correlated very well with those obtained from standard laboratory measurements. Therefore, the changes in viscosity due to reduction in the molecular weight of the polymer, as measured with the miniature devices, can be used to assess whether degradation has taken place. A miniaturized electrical measurement platform was then tested in comparable polymer fluids for use in the field and obtained comparable results.\u0000 The platforms described here provide a simple, cost-effective, and user-friendly platform for the detection of polymer degradation in the field, thus providing valuable information in real-time during costly polymer flooding operations.","PeriodicalId":113697,"journal":{"name":"Day 2 Tue, October 04, 2022","volume":"31 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":"126368721","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 issue of screen erosion is a complex problem that doesn't lend itself very easily to modeling and computer aided design, particularly when it comes to metal mesh screens. Interactions between solid and liquid (settling/suspension) and between the solids and the screen material (plugging) are complex and evolving over time due to wear and fluctuations associated with multiphase flow or other reservoir related changes over the life of the well. As a result, screen development is best performed using pilot testing to simulate downhole conditions and optimize the design. In that regard, the setting of standard performance tests is essential. A series of time lapse erosion tests performed on mesh screens recently highlighted the benefits of shielding the screen from the basepipe perforations to improve erosion resistance. This new feature provided several fold improvements in the mesh screen erosion resistance and was implemented in a novel screen design. It consists in placing a partially perforated inner shroud underneath a regular screen cartridge, with blind spots precisely located over the basepipe holes to prevent direct line of sight flow and reducing local velocity by diffusing flow across the entire screen area. An extended continuous erosion test was used to validate the design and qualify metal meshes, and mechanical testing as per the new API19ss standard for sand control screens was performed to qualify the new screen and demonstrate its reliability. Comparing the performance of the new screen design against similarly built screens confirmed that the addition of the new diffusion shroud does not adversely impact the mechanical performance of the screen while imparting improved erosion resistance to the screen.
{"title":"Design and Qualification of a New Erosion Resistant Sand Control Screen","authors":"C. Malbrel, Edward Blackburne","doi":"10.2118/209951-ms","DOIUrl":"https://doi.org/10.2118/209951-ms","url":null,"abstract":"\u0000 The issue of screen erosion is a complex problem that doesn't lend itself very easily to modeling and computer aided design, particularly when it comes to metal mesh screens. Interactions between solid and liquid (settling/suspension) and between the solids and the screen material (plugging) are complex and evolving over time due to wear and fluctuations associated with multiphase flow or other reservoir related changes over the life of the well. As a result, screen development is best performed using pilot testing to simulate downhole conditions and optimize the design. In that regard, the setting of standard performance tests is essential.\u0000 A series of time lapse erosion tests performed on mesh screens recently highlighted the benefits of shielding the screen from the basepipe perforations to improve erosion resistance. This new feature provided several fold improvements in the mesh screen erosion resistance and was implemented in a novel screen design. It consists in placing a partially perforated inner shroud underneath a regular screen cartridge, with blind spots precisely located over the basepipe holes to prevent direct line of sight flow and reducing local velocity by diffusing flow across the entire screen area.\u0000 An extended continuous erosion test was used to validate the design and qualify metal meshes, and mechanical testing as per the new API19ss standard for sand control screens was performed to qualify the new screen and demonstrate its reliability.\u0000 Comparing the performance of the new screen design against similarly built screens confirmed that the addition of the new diffusion shroud does not adversely impact the mechanical performance of the screen while imparting improved erosion resistance to the screen.","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":"130102498","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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