SPE Journal最新文献

{"title":"Failure Forensics of Shaped PDC Cutters Using Image Analysis and Deep Learning","authors":"Wei Liu, Jianchao Li, Deli Gao","doi":"10.2118/218383-pa","DOIUrl":"https://doi.org/10.2118/218383-pa","url":null,"abstract":"\u0000 One of the major advances in polycrystalline diamond compact (PDC) bits in the last 10 years is the global adoption of 3D-shaped PDC cutters. By manipulating the cutter shape based on the understandings of cutter–rock interaction mechanisms, the cutting efficiency and mechanical properties of PDC cutters have been greatly improved. Ongoing innovations in 3D-shaped PDC cutter technology are critical to overcoming the more and more challenging formations in ultradeep wells, such as the 10 000-m-deep wells being drilled in China. Such an important role for 3D-shaped PDC cutters in oil and gas drilling applications necessitates a complete and effective failure analysis method. However, the current International Association of Drilling Contractors (IADC) dull grading cannot fulfill this objective. It is out of date in judging the damages to PDC bits and exhibits more limitations in addressing the unique challenges presented by complicated cutter shapes.\u0000 To address this issue, an intelligent recognition model for PDC bit damage identification was developed based on the image analysis technology and the YOLOv7 algorithm. More than 10,000 dull bit images were used to train and validate this intelligent recognition model, which were collected from 363 PDC bits that suffered different degrees of damage after being used to drill 185 wells in the Sinopec Shengli Oilfield. Compared to the existing models, the developed intelligent recognition model has several notable contributions. First, the developed model is capable of recognizing the damages of various shaped PDC cutters commonly used by the global bit manufacturers, enabling a more accurate assessment of the failure behaviors of shaped cutters and their bits. The detection accuracy of the developed model exceeds 80% based on the confusion matrix. The recognition results by the developed artificial intelligence (AI) model are consistent with the actual failure modes judged by experienced drilling engineers. Second, the developed AI model provides direct qualitative identification of the failure modes and failure reasons for both cutters and PDC bits rather than the quantitative evaluation of the missing diamond layer used by IADC dull grading. Furthermore, the developed model eliminates the effect of reclaimed cutters on the AI detection results based on the implicit use of spatial cues in the YOLOv7 algorithm. The intelligent recognition model developed in this work can provide reliable and valuable guidance for the post-run evaluation, the bit selection for the next run, and the iterative optimization of bit design.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"362 3","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021699","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}

{"title":"A New Method to Reduce Shale Barrier Effect on SAGD Process: Experimental and Numerical Simulation Studies using Laboratory-Scale Model","authors":"Xiaohu Dong, Huiqing Liu, Yunfei Tian, Siyi Liu, Jiaxin Li, Liangliang Jiang, Zhangxin Chen","doi":"10.2118/218390-pa","DOIUrl":"https://doi.org/10.2118/218390-pa","url":null,"abstract":"\u0000 Shale barrier has been widely reported in many steam-assisted gravity drainage (SAGD) projects. For an SAGD project, the properties and distribution of shale barrier can significantly impede the vertical expansion and lateral spread of steam chamber. Currently, although some literature has discussed the shale barrier effect from different perspectives, a systematic investigation combining the scaled physical and numerical simulations is still lacking. Simultaneously, how to reduce the shale barrier effect is also challenging. In this study, aiming at the Long Lake oilsands resources, combining the methods of 3D experiment and numerical simulation, a new method based on a top horizontal injection well is proposed to reduce the impact of shale barrier on the SAGD process. First, based on a dimensionless scaling criterion of gravity-drainage process, we conducted two 3D gravity-drainage experiments (base case and improved case) to explore the effect of shale barrier and the performance of top injection well on SAGD production. During experiments, to improve the similarity between the laboratory 3D model and the field prototype, a new wellbore model and a physical simulation method of shale barrier are proposed. The location of the shale barrier is placed above the steam injection well, and the top injection well is set above the shale barrier. For an improved case, once the steam chamber front reaches the horizontal edge of the shale barrier, the top injection well can be activated as a steam injection well to replace the previous steam injection well in the SAGD well pair. From the experimental observation, the effect of the top injection well is evaluated. Subsequently, a set of numerical simulation runs are performed to match the experimental measurements. Therefore, from this laboratory-scale simulation model, the effect of shale barrier size is discussed, and the switch time of the top injection well is also optimized to maximize the recovery process. Experimental results indicate that a top injection well-based oil drainage mode can effectively unlock the heavy crude oil above shale barrier and improve the entire SAGD production. Compared with a basic SAGD case, the top injection well can increase the final oil recovery factor by about 8%. Simultaneously, through a mass conservation law, it is calculated that the unlocking angle of remaining oil reserve above the shale barrier is about 6°. The angle can be used to effectively evaluate the recoverable oil reserve after the SAGD process for the heavy oil reservoir with a shale barrier. The simulation results of our laboratory-scale numerical simulation model are in good agreement with the experimental observation. The optimized switch time of the top injection well is the end of the second lateral expansion stage. This paper proposes a new oil drainage mode that can effectively reduce the shale barrier effect on SAGD production and thus improve the recovery performance of heavy oil reservoirs.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"23 30","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624945","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}

{"title":"A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification","authors":"J. Bi, Jing Li, Keliu Wu, Zhangxin Chen, Shengnan Chen, Liangliang Jiang, Dong Feng, Peng Deng","doi":"10.2118/218386-pa","DOIUrl":"https://doi.org/10.2118/218386-pa","url":null,"abstract":"\u0000 Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate models suffer from limitations in autonomous temporal information learning and restrictions in generalization potential, which is due to a lack of integration with physical knowledge. In response to these challenges, a physics-informed spatial-temporal neural network (PI-STNN) is proposed in this work, which incorporates flow theory into the loss function and uniquely integrates a deep convolutional encoder-decoder (DCED) with a convolutional long short-term memory (ConvLSTM) network. To demonstrate the robustness and generalization capabilities of the PI-STNN model, its performance was compared against both a purely data-driven model with the same neural network architecture and the renowned Fourier neural operator (FNO) in a comprehensive analysis. Besides, by adopting a transfer learning strategy, the trained PI-STNN model was adapted to the fractured flow fields to investigate the impact of natural fractures on its prediction accuracy. The results indicate that the PI-STNN not only excels in comparison with the purely data-driven model but also demonstrates a competitive edge over the FNO in reservoir simulation. Especially in strongly heterogeneous flow fields with fractures, the PI-STNN can still maintain high prediction accuracy. Building on this prediction accuracy, the PI-STNN model further offers a distinct advantage in efficiently performing uncertainty quantification, enabling rapid and comprehensive analysis of investment decisions in oil and gas development.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"1 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015641","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}

{"title":"Failure Probability Prediction for Offshore Floating Structures Using Machine Learning","authors":"H. Lim","doi":"10.2118/218408-pa","DOIUrl":"https://doi.org/10.2118/218408-pa","url":null,"abstract":"\u0000 Accurately estimating the failure probability is crucial in designing civil infrastructure systems, such as floating offshore platforms for oil and gas processing/production, to ensure their safe operation throughout their service periods. However, as a system becomes complex, the evaluation of a limit state function may involve the use of an external computer solver, resulting in a significant computational burden to perform Monte Carlo simulations (MCS). Moreover, the high-dimensionality of the limit state function may limit efficient sampling of input variables due to the “curse of dimensionality.” To address these issues, an efficient machine learning framework is proposed, combining polynomial chaos expansion (PCE) and active subspace. This will enable the accurate and efficient evaluation of the failure probability of an offshore structure, which typically involves a large number of uncertain parameters. Unlike conventional PCE schemes that use the original random variable space or the auxiliary variable space for building a surrogate model, the proposed method utilizes a reduced-dimension space to circumvent the “curse of dimensionality.” An appropriate coordinate transformation is first sought so that most of the variability of a limit state function can be accounted for. Next, a PCE surrogate limit state function is constructed on the derived low-dimensional “active subspace.” The Gram-Schmidt orthogonalization process is used for making basis polynomial functions, which is particularly effective when input random parameters do not follow the Askey scheme and/or when a dependence structure between the input parameters exists. Therefore, a nonlinear iso-probabilistic transformation, which makes the convergence of a surrogate to the true model difficult, is not required, unlike traditional PCE. Numerical examples, including limit state functions related to structural dynamics problems, are presented to illustrate the advantages of the proposed method in estimating failure probabilities for complex structural systems. Specifically, the method exhibits significantly improved efficiency in estimating the failure probability of an offshore floating structure without compromising accuracy as compared to traditional PCE and MCS.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"208 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026641","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}

{"title":"An Investigation on the Impact of Submicron-Sized Bubbles on the Fragmentation of Methane Clathrates Using Molecular Dynamics Simulation","authors":"John Michael Tesha, Derrick S. Dlamini, Edgar Christian Mapunda, Ashura Katunzi Kilewela","doi":"10.2118/218399-pa","DOIUrl":"https://doi.org/10.2118/218399-pa","url":null,"abstract":"\u0000 The formation of submicron-sized bubbles is frequently associated with the fragmentation of methane clathrate. A bubble refers to a pocket or a round particle of one substance trapped inside another. In most cases, these spherical pockets are made of gas trapped inside of a liquid. Usually, bubbles can lie underneath the surface of the liquid until the surface tension breaks and the gas escapes back into the atmosphere. Therefore, understanding the fluid dynamics behavior of the clathrate phase shift and enhancing the production efficiency of natural gas requires knowledge of the impact of submicron-sized bubbles on the clathrate fragmentation. In this scenario, molecular dynamics simulation (MDS) models were carried out to investigate the methane clathrate fragmentation rate with and without preexisting submicron-sized bubbles. The findings demonstrate layer-by-layer fragmentation of the methane clathrate cluster in the liquid phase. Furthermore, this mechanism shows temperature and submicron-sized bubble existence independent of simulation settings or conditions. However, because of the stability of the supersaturated methane solution for a long period, methane clathrate fragmentation does not always result in the formation of submicron-sized bubbles. It was observed that between the bubble (submicron-size) of methane and the cluster surface of methane clathrate, there is a steep slope of methane concentration. This results in the liquid phase efficiently decreasing the methane concentration and improving the migration of natural gas in different directions, hence the driving force increases for methane clathrate fragmentation. Our discoveries in this research show that the existence of submicron-sized bubbles near the surface of the methane clathrate can speed up the rate of intrinsic decomposition while decreasing the activation energy of methane clathrate fragmentation. The mass flow rate toward submicron-sized bubbles linearly correlates with the methane clathrate fragmentation rate. The mass flow rate is governed by the size of the submicron-sized bubbles and the spacing between the methane clathrate submicron-sized bubbles. Our results contribute to the in-depth knowledge of the fragmentation technique in the liquid phase for methane clathrates, which is critical in optimizing and designing effective gas clathrate development methods.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"10 8","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015877","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}

{"title":"Development of Novel Delayed Swelling Polymer Gel Particles with Salt Resistance for Enhanced In-Depth Permeability Control","authors":"Yining Wu, Haiqing Zhang, Liyuan Zhang, Yongping Huang, Mingwei Zhao, Caili Dai","doi":"10.2118/218394-pa","DOIUrl":"https://doi.org/10.2118/218394-pa","url":null,"abstract":"\u0000 Prolonged waterflooding or polymer flooding in oil fields often exacerbates reservoir heterogeneity, leading to premature water breakthrough and high water cut, which significantly hinders efficient oilfield development. To address this issue, polymer gel particles have been prescribed to enhance sweep efficiency and augment waterflooding recovery by plugging preferential pathways within the reservoir. However, inherent weaknesses of polymer gel particles, such as fast water absorption and expansion rates in the initial stage and low post-expansion rates, make it difficult to balance in-depth transportation and plugging performance. Additionally, these gel particles are sensitive to ions in the formation water, resulting in reduced expansion rates under high-salinity conditions. Therefore, there are still challenges in the application of polymer gel particles for in-depth permeability control. In this study, a new type of delayed swelling and salt-resistant polymer gel particle was synthesized through inverse emulsion copolymerization. To achieve delayed swelling, we use a degradable crosslinker and hydrophobic monomer to enhance the crosslinked network density and hydrophobicity of gel particles. Our double crosslinked gel particles keep their original size until Day 2, then gradually swell up to 20 days in NaCl solution with a concentration of 15×104 mg·L−1 at 90°C. In comparison, the traditional single crosslinked gel particles show significant disparities in swelling behaviors and quickly swell when just dispersed in a 15×104 mg·L−1 NaCl solution at 90°C, maintaining roughly the same size over the testing period. Coreflooding experiments demonstrate that the residual resistance before and after aging increases from 2.37 to 6.82. The newly synthesized delayed swelling and salt-resistant polymer gel particles exhibit promising potential for overcoming the challenges associated with reservoir heterogeneity and high salinity.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"101 8","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139020137","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}

{"title":"Ultrahigh-Resolution Reconstruction of Shale Digital Rocks from FIB-SEM Images Using Deep Learning","authors":"Yipu Liang, Sen Wang, Qihong Feng, Mengqi Zhang, Xiaopeng Cao, Xiukun Wang","doi":"10.2118/218397-pa","DOIUrl":"https://doi.org/10.2118/218397-pa","url":null,"abstract":"\u0000 Accurate characterization of shale pore structures is of paramount importance in elucidating the distribution and migration mechanisms of fluids within shale rocks. However, the acquisition of high-resolution (HR) images of shale rocks is limited by the precision of the scanning equipment. Even with higher-precision devices, compromising the image field of view becomes inevitable, making it challenging to faithfully represent the actual conditions of shale. We propose a stepwise 3D super-resolution (SR) reconstruction method for shale digital rocks based on the widely used focused-ion-beam scanning electron microscope (FIB-SEM) technique. This method effectively addresses the issues of inconsistent horizontal and vertical resolutions as well as low 3D image resolution in FIB-SEM images. By adopting this approach, we significantly enhance image details and clarity, enabling successful observations of pores smaller than 10 nm within shale and laying a foundation for further pore-scale flow simulations. Furthermore, we extract the pore network model (PNM) from the SR reconstructed digital rock to analyze the pore size distribution, coordination number, and pore-throat ratio of shale samples from the Jiyang Depression. The results demonstrate a pore radius distribution in the range of 0 nm to 40 nm, which aligns with the results from nitrogen adsorption experiments. Notably, pores with radii smaller than 10 nm account for 50% of the total connected pores. The proportion of isolated pores in the SR reconstructed shale PNM is significantly reduced, with the coordination number mainly distributed between 1 and 4. The pore-throat ratio of shale ranges from 1 to 3, indicating a relatively uniform development of pores and throats. This study introduces a novel method for accurately characterizing the shale pore structure, which aids researchers in evaluating the pore size distribution and connectivity of shales.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"206 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138992493","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}

{"title":"New Approach to Encapsulation for Controlled Release of Chemical Additives","authors":"Elizabeth Q. Contreras","doi":"10.2118/213736-pa","DOIUrl":"https://doi.org/10.2118/213736-pa","url":null,"abstract":"\u0000 A new approach for the synthesis of bio-inspired polymer microcapsules used to encapsulate chemical additives and designed for small molecule release and delivery is shown here. The flexibility to design different microcapsules using an emulsion template results in various encapsulated additives for a new polymer technology platform. The base materials for encapsulation are polyaramids that are highly crosslinked membrane shells around an empty core. These empty capsules provide a carefully designed space to site-isolate chemical additives, various encapsulants for encapsulation, and delivery where needed. These microcapsules have demonstrated that after being formed from a simple one-pot synthesis between two immiscible solutions, a new method for encapsulation for applications in ordinary Portland cement is possible. The final product is a free-flowing solid that can be easily added to any fluid application. Experimental results show that when added to a basic cement slurry design, cement responds to the release of a salt accelerant as measured using standard oilfield equipment, like the pressurized consistometer, which measures changes in viscosity and thickening times. In one of many applications, the consistency of cement remains favorable at 20 Bc after adding encapsulated calcium chloride for up to 5 hours, for example. Over time, various capsules caused cement slurries to set at right angles at various thickening times with the controlled release of encapsulated calcium chloride. This new approach for encapsulation is promising for the chemical and energy field.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"942 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139019015","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}

{"title":"A Novel Evaluation Technique for Mudcake of Drilling Fluid Based on the Nuclear Magnetic Resonance Method","authors":"Yunxing Duan, Xianshu Dong, Yang Hao, Yuping Fan, Xiaomin Ma, Lu Zhou","doi":"10.2118/217995-pa","DOIUrl":"https://doi.org/10.2118/217995-pa","url":null,"abstract":"\u0000 In drilling engineering, mudcake is formed when the drilling fluid invades the near-wellbore zone, which can reduce drilling fluid leakage and reservoir pollution and maintain wellbore stability. Exploring the method that can best represent the field working conditions to evaluate the mudcake is an urgent problem. At present, the macro-evaluation method cannot describe the characteristics of heterogeneity, particle accumulation, and porous media of mudcake. The micro-evaluation method needs local sampling, drying, curing, slicing, and other tedious disturbance treatments, which cannot reflect the overall characteristics of mudcake. To solve these problems, a novel technique for evaluating the pore structure of mudcake was established by taking nuclear magnetic resonance (NMR) T2 tests as the key mean and integrating high-pressure mercury injection tests, fluid isotope tracing, dynamic/static filtration experiment of drilling fluid, pore permeability parameter tests of core, and particle-size distribution tests of drilling fluid. The evaluation results of mudcake formed by drilling fluid static and dynamic filtration show that this technology can study the pore structure characteristics of the outer mudcake and intruded core and the distribution characteristics of the inner mudcake and filtrate in the intruded core. The novel evaluation technique has strong operability and less demand for experimental samples, which can study the micron-scale pore structure of mudcake and provide practical methods for drilling fluid system optimization and application effect evaluation, reservoir damage evaluation, and development or verification of the filtration model.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"269 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139023798","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}

{"title":"A New Approach to Apply Decline-Curve Analysis for Tight-Oil Reservoirs Producing Under Variable Pressure Conditions","authors":"Leopoldo Matias Ruiz Maraggi, Mark P. Walsh, Larry W. Lake","doi":"10.2118/218016-pa","DOIUrl":"https://doi.org/10.2118/218016-pa","url":null,"abstract":"<p>Decline-curve models inherently assume that the bottomhole flowing pressure (BHP) is constant. This is a poor assumption for many unconventional wells. For this reason, the application of decline-curve models might lead to incorrect flow regime identification and estimated ultimate recovery (EUR). This work presents a novel technique that combines variable BHP conditions with decline-curve models and compares its results with traditional decline-curve analysis (DCA) for both synthetic and tight-oil wells.</p><p>Using superposition, we generate a synthetic rate example using the constant-pressure solution of the diffusivity equation for a slightly compressible fluid (decline-curve model) along with a BHP history. However, we validate the technique using bottomhole and initial reservoir pressures that contain errors. The algorithm consists of three sequential optimizations. In each optimization, the algorithm estimates (1) the decline-curve model parameters, (2) the BHP, and (3) the initial reservoir pressure. The result of the synthetic example leads to an accurate production history match and corrected estimates of the initial reservoir pressure and the BHP history. Finally, we compare the results of the technique with traditional DCA in terms of (a) the model parameters, (b) flow regime identification, (c) production history matches, and (d) EUR for tight-oil wells using three decline-curve models: 1D single-phase constant-pressure solution of the diffusivity equation for a slightly compressible fluid, logistic growth model, and Arps hyperbolic relation.</p><p>For the synthetic case, the algorithm estimates the model parameters and the true initial reservoir pressure within a 2% error. In addition, the method regenerates the true BHP history and provides an excellent production history match. The analysis of the tight-oil wells shows that the new approach clearly identifies the flow regimes present in the well, which can be difficult to detect using traditional DCA when the BHP varies. In contrast, the application of traditional DCA shows considerable errors in the estimation of the model’s parameters and a poor history match of the production data. Finally, this work shows that incorporating variable BHP into the decline-curve models leads to more accurate production history matches and EUR values compared to using only rate-time data.</p><p>This paper illustrates a workflow that incorporates variable BHP conditions for any decline-curve model. Moreover, the approach can handle errors in both the BHP and the initial reservoir pressure and provides corrected estimates of these variables. The technique is computationally fast and history matches and forecasts the production of unconventional wells more accurately than traditional DCA. The major contribution of this work is the remarkable simplicity yet robustness of our solution to variable-pressure DCA. Finally, we developed a web-based application to provide the readers with a hands-on exper","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"34 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140126559","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}