Day 2 Wed, January 25, 2023最新文献

{"title":"A Hybrid Neural Workflow for Optimal Water-Alternating-Gas Flooding","authors":"Gurpreet Singh, Davud A. Davudov, E. Al-Shalabi, A. Malkov, A. Venkatraman, Ahmed Mansour, Rosemawati Abdul-Rahman, Dr. Bhumika Das","doi":"10.2118/212641-ms","DOIUrl":"https://doi.org/10.2118/212641-ms","url":null,"abstract":"\u0000 Water-alternating-gas (WAG) injection is a gas-based enhanced oil recovery (EOR) technique used to overcome problems related with gas injection including gravity override, viscous fingering, and channeling. The WAG EOR technique is used to control gas mobility, which boosts project economics. Water alternating gas (WAG) has the dual benefit of higher recovery than continuous gas injection and CO2 sequestration. Higher sweep efficiencies and conformance control have been shown to increase the life cycle net present value (NPV) for improved field development and deployment planning. Nevertheless, a poor WAG design often results in unfavorable oil recovery. This study investigates WAG optimization in a sandstone field using a hybrid numerical-machine learning (ML) model. In this work, we present a hybrid neural approach for optimizing the WAG injection process that can be easily integrated as a workflow with any existing reservoir simulator for optimal WAG parameters to maximize reservoir life cycle cumulative recoveries.\u0000 The reservoir simulator is treated as a sample generator to form an ensemble of recovery scenarios with the WAG parameters as inputs to a dense neural network (DNN) and outputs/labels as cumulative recoveries. The neural network then serves two roles: 1) a readily available map between WAG parameters and cumulative recoveries for reduced computational cost and hence faster on-demand evaluation, and 2) as a repository condensing important correlations that can be appended with additional samples or reduced by removing redundant samples (simulation runs). Consequently, the hybrid neural approach also provides a clear picture of which simulation runs (or samples) are more conducive to optimal recovery predictions for an effective strategy to sample the high dimensional WAG parameter space and reduced compute times. This becomes especially important when we consider field scale optimization scenarios with multiple wells each with their separate injection schedules requiring exponentially increasing samples with a brute force ensemble approach (add an example in the introduction section or later and cross-refer here).","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132943424","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}

{"title":"Numerical Investigation of Hybrid Smart Water and Foam Injections in Carbonate Reservoirs","authors":"A. Hassan, B. N. Tackie-Otoo, M. Ayoub, M. Mohyaldinn, E. Al-Shalabi, Imad A. Adel","doi":"10.2118/212663-ms","DOIUrl":"https://doi.org/10.2118/212663-ms","url":null,"abstract":"\u0000 This contribution is a progressive effort to investigate the effect of the novel hybrid EOR method of Smart Water Assisted Foam (SWAF) technique on oil recovery from carbonates through numerical modeling. In this work, a core-scale model was utilized to provide an insight and a better understanding of the controlling mechanisms behind incremental oil recovery using a new hybrid EOR method consisting of a combination of smart water flooding and foam injection, termed as Smart Water Assisted Foam (SWAF) technology, particularly for carbonate reservoirs. A core-scale model encapsulating the physics of SWAF flooding was used to history-match experimental data and the model was further optimized utilizing the CMG simulator. For extracting the most value from this numerical investigation, a sensitivity analysis was performed to monitor the effect of influential parameters affecting oil recovery depending on the spectrum of the experimental data available. The objective functions used in the sensitivity analysis include minimizing the history-matching global error and maximizing the oil recovery profiles. Three sensitivity analysis approaches were used: Tornado-plot, SOBOL analysis, and MORRIS analysis. For generating the related proxy models, polynomial regression, and radial basis function (RBF) neural networks were investigated. Subsequently, the DECE-based and PSO-based optimization methods were employed to examine the effect of chemical design parameters such as smart water (Mg2+), surfactant aqueous solution (SAS), and foam concentrations along with the liquid production rate on the oil recovery factor during SWAF-flooding.\u0000 Based on the numerical results, the experimental coreflooding data were accurately history-matched using the proposed model with a minimal error of 4.74% applying the PSO-based optimization method. Furthermore, in terms of the objective function prediction during the sensitivity analysis study, the comparative assessment of both proxy models on the verification plot reveals that the RBF neural network outperforms the polynomial regression. Consolidated findings from the three sensitivity analyses, i.e., the Tornado-plot, SOBOL, and MORRIS, outline three common parameters that significantly affect the oil recovery profiles that are liquid production rate (LigProdCon), foam (DTRAPW SAS2), and Mg2+ concentration (DTRAP Mg3) parameters. On the other hand, in terms of maximizing the oil recovery while minimizing the usage of injected chemicals during SWAF flooding, the optimal solution via the PSO-based approach is superior (97.89%) to the DECE-based optimal solutions (92.47%). This work presents one of the few studies investigating the numerical modeling of the SWAF process and capturing its effects on oil recovery. The optimized core scale model can be further used as a base for building a field-scale model and designing a successful pilot project.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133398715","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}

{"title":"CO2 Injection in a Depleted Gas Field","authors":"Markus Lüftenegger, A. Rath, Michael Smith, Alexey Danilko","doi":"10.2118/212589-ms","DOIUrl":"https://doi.org/10.2118/212589-ms","url":null,"abstract":"\u0000 A compositional dynamic simulation model is fully implicitly integrated with a gas injection surface network model, to study the effects of CO2 injection into a depleted gas field. Multiple prediction scenarios are evaluated, under uncertainty, to reduce risk and improve decision making. We propose a workflow, composed of a geological sensitivity clustering step followed by a dynamic calibration step. The aim of this workflow is to decrease the objective function and improve the reliability of a probabilistic forecast, to model the CO2 storage potential of an onshore depleted gas field. Each run, containing all parameters and its objective function was exported and introduced into an inhouse R Script. Within this script we train a random forest tree to predict the objective function for various parameter combinations. This random forest is then used to generate 1 million models with the initial distribution from the simulation runs and will predict their objective function. The idea here is to get to a posterior distribution that can be used in the second simulation iteration. This method achieves a better history match within the ensemble, in a vastly reduced timeframe. History matched models were taken forward to predict CO2 injectivity. Injection variables, facilities and well completions for several wells have been included in the analysis, and numerical reservoir simulation models have been integrated with a surface network.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"91 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129983542","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}

{"title":"Internally Catalyzed Aqueous-Based Emulsion of Curable Epoxy Resin Sand Consolidation Treatment Extends Economical Production in Austria's Mature Oil and Gas Fields.","authors":"Erika Johana Tovar Trujillo, Y. Santin, Obinnaya Ukoha, Riccardo Caldarelli, R. Maier, A. Kiss, Monica Moertl, David Zabel","doi":"10.2118/212682-ms","DOIUrl":"https://doi.org/10.2118/212682-ms","url":null,"abstract":"\u0000 Sand production is one of the major challenges for mature fields in Austria. With increasing water production, the severity of the sand migration augments, leading to the shut-in of the wells. Eliminating or substantially reducing sand production at the sand face is the most viable option to continue hydrocarbon production. The project's target was to research and apply a technically sound solution readily available in Europe, with reduced HSSE risks and little economic impact.\u0000 To control intervention costs, it was decided to favor sand control solutions for rig-less interventions. Collaboratively, the teams evaluated formation rock consolidation with the help of an internally catalyzed aqueous-based emulsion of curable epoxy resin (ICABECER). Laboratory testing demonstrated the system's suitability for the target wells and confirmed the viability of the planned operations scheduled to deploy the treatment via coiled tubing (CT), as well as limiting concerns about permeability reduction. Finally, field operations of the application, clean-up, and production face were monitored and evaluated.\u0000 The major concern when using resins to agglomerate sand grains in a reservoir rock is that the pore space is reduced, jeopardizing the rock permeability. Laboratory testing confirmed that the permeability of the rock can be retained. Due to the simplicity of the intervention, the treatment could be deployed with standard equipment keeping it within the budgetary constraints of very mature fields.\u0000 To mitigate possible risks, wells having challenging production backgrounds and scheduled for plug and abandon were selected. In these wells, previous conventional sand control measures failed, such as gravel pack installations or attempting to produce sand and separate it on surface. Post-job results demonstrated that the in-situ consolidation generated a reduction of sand content to a level allowing production of the wells. During the clean-up period of the gas well, sufficient sand was produced to erode the choke. After the well start-up period, sand production was eliminated, and the well was returned to the target rate. Monitoring of solid contents in the flow and the evaluation of coupons confirmed the suitability of the technique to establish flow with acceptable risks contributing to economic success.\u0000 The cost-effective ICABECER chemical treatment, along with the methodology, opens new opportunities for the asset to prolong well life and increase the overall recovery factor from the reservoir. Technical simplicity and the reduced environmental impact of the chemicals are key for resource-saving and sustainable operations in mature fields.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128986743","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}

{"title":"Application of Machine Learning in a Giant Mature Reservoir to Speed-Up Infill Prospects Screening, Optimize Field Development and Improve the Ultimate Recovery Factor","authors":"C. Fabbri, N. Reddicharla, Wen Shi, Alaa Al Shalabi, Sara Al Hashmi, Sulaiman Al Jaberi","doi":"10.2118/212678-ms","DOIUrl":"https://doi.org/10.2118/212678-ms","url":null,"abstract":"\u0000 In giant reservoirs, production sustainability strongly depends on the identification of opportunities for infill drilling. This paper presents the use of Machine Learning to speed-up and improve the efficiency of the evaluation of future infill wells, in an effort to optimize field development of a Giant Mature reservoir Onshore Abu Dhabi.\u0000 In the mature giant carbonate reservoir studied, more than 420 wells are already drilled with consistent spacing but with varying orientations. This paper illustrates some examples of settings that are difficult to assess without geometric calculations, leading to time-consuming opportunity identification and classification.\u0000 The minimum set of input for the program includes existing wells trajectories, faults polygons, contact, and production data. Users can define the minimum drainage area for each well, maturity criteria and drain length. For each subsurface target identified, a polygon and simulation input are generated. The Python program is developed and run on an in-house platform and solve the future wells positioning in three main steps: (1) Geometric screening and identification of locations with required spacing, (2) Analysis of nearby well performance, (3) automatic generation of simulation input for evaluation of the subsurface target.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129112469","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}

{"title":"Physics Informed Machine Learning for Production Forecast","authors":"R. Manasipov, Denis Nikolaev, Dmitrii Didenko, Ramez Abdalla, M. Stundner","doi":"10.2118/212666-ms","DOIUrl":"https://doi.org/10.2118/212666-ms","url":null,"abstract":"\u0000 Understanding the reservoir behavior is vital knowledge required for various aspects of the reservoir management cycle such as production optimization and establishment of the field development strategy. Reservoir simulation is the most accurate tool for production forecast, but often it is very expensive from aspects of computational time and investment in the model building process. In this work, the machine learning methods for accurate production forecast that honor the material balance constraints are presented.\u0000 The presented hybrid model approach consists of several main components. The material balance constraints are necessary during the training process to avoid unphysical solutions and to honor conservation laws. For this reason, the Capacitance Resistance Model (CRM) was chosen due to its intuitive form and flexibility in describing reservoirs of various complexities. Another part of the solution is represented by powerful machine learning methods such as Generalized Additive Models (GAM), Gradient Boosting, and Convolutional and Recurrent Neural Networks. Neural Networks and Gradient Boosting methods are very popular machine learning techniques. However, in this work, it is demonstrated that GAM can also produce results comparable to the former methods while holding additional attractive properties. The basis functions of GAM are the splines, which are smooth functions with continuous derivatives. Such properties are very useful for optimization tasks. GAM is an extension of standard Generalized Linear Models (GLM), which provides rich tools for model explainability. It is hence also advantageous for the understanding how the reservoir behaves through such models.\u0000 The implemented approach was applied to the publicly available data with an existing history matched reservoir model for the offshore field with several injectors and producers. This allowed us to compare results and build machine learning models that describe communication between wells and can be further analyzed though the simulation model.\u0000 Machine learning methods are constantly improving at solving difficult problems, while it often suffers from nonphysical solutions and unexplainable models. The presented method holds the properties of explainable regression models while providing powerful predictability capabilities within material balance constraints. By no means does it try to replace the reservoir simulation but offers a complementary solution, which is reliable and necessary in cases where there is no full reservoir model available.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129849939","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}

{"title":"Identifying New Behind Casing Opportunities Using Machine Learning","authors":"I. M. Fadhil, J. Shah, Salmi Sansudin, A. Abdollahzadeh, Husni Husiyandi, Nur Aimi Azimah Azizul, Fairuz Hidayah Hasnan, Yuan Jiun Thai","doi":"10.2118/212627-ms","DOIUrl":"https://doi.org/10.2118/212627-ms","url":null,"abstract":"\u0000 This paper discusses the adoption of Machine Learning (ML) approach to identify new Behind Casing Opportunities (BCO) in two brown fields (B and S) offshore East Malaysia. A multi-stage field-based ML models were developed based on selected wells and consequently used to predict reservoir characteristics in completed wells. The predicted results indicated new upside BCO for add perforation candidate.\u0000 Raw and interpreted data from B and S fields were analyzed and processed for model training and evaluation. For the case of identifying new opportunity, a specific model development strategy and train dataset selection was employed. The trained ML models evaluated to select the optimal models to predict lithologies, porosity, permeability and water saturations which are then been compared against the actual interpretation. Eventually, the identified upside potentials are validated by Subject Matter Experts (SME) before being proposed as add perforation candidate.\u0000 It was observed that the models’ performances vary between the two fields due to unique geological complexity as well as the varying quality of raw and interpreted data from each field. Field B which is more geologically complex performs less compared to Field S.\u0000 In conclusion, this study provides and insight on the advantages and limitations of machine learning to identify new upside BCO in completed wells. The novelty in this work is in the specific model development strategy to identify new upside BCO potentials.\u0000 This work may be beneficial and essential especially in enhancing resource monetization in brown fields which face challenges in terms of high idle well percentage, low recovery, and declining production.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124679328","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}

{"title":"Reservoir Modeling and Optimization Based on Deep Learning with Application to Enhanced Geothermal Systems","authors":"B. Yan, Zhen Xu, Manojkumar Gudala, Zeeshan Tariq, T. Finkbeiner","doi":"10.2118/212614-ms","DOIUrl":"https://doi.org/10.2118/212614-ms","url":null,"abstract":"\u0000 With the energy demand arising globally, geothermal recovery by Enhanced Geothermal Systems (EGS) becomes a promising option to bring a sustainable energy supply and mitigate CO2 emission. However, reservoir management of EGS primarily relies on reservoir simulation, which is quite expensive due to the reservoir heterogeneity, the interaction of matrix and fractures, and the intrinsic multi-physics coupled nature. Therefore, an efficient optimization framework is critical for the management of EGS.\u0000 We develop a general reservoir management framework with multiple optimization options. A robust forward surrogate model fl is developed based on a convolutional neural network, and it successfully learns the nonlinear relationship between input reservoir model parameters (e.g., fracture permeability field) and interested state variables (e.g., temperature field and produced fluid temperature). fl is trained using simulation data from EGS coupled thermal-hydro simulation model by sampling reservoir model parameters. As fl is accurate, efficient and fully differentiable, EGS thermal efficiency can be optimized following two schemes: (1) training a control network fc to map reservoir geological parameters to reservoir decision parameters by coupling it withfl ; (2) directly optimizing the reservoir decision parameters based on coupling the existing optimizers such as Adam withfl.\u0000 The forward model fl performs accurate and stable predictions of evolving temperature fields (relative error1.27±0.89%) in EGS and the time series of produced fluid temperature (relative error0.26±0.46%), and its speedup to the counterpart high-fidelity simulator is 4564 times. When optimizing withfc, we achieve thermal recovery with a reasonable accuracy but significantly low CPU time during inference, 0.11 seconds/optimization. When optimizing with Adam optimizer, we achieve the objective perfectly with relatively high CPU time, 4.58 seconds/optimization. This is because the former optimization scheme requires a training stage of fc but its inference is non-iterative, while the latter scheme requires an iterative inference but no training stage. We also investigate the option to use fc inference as an initial guess for Adam optimization, which decreases Adam's CPU time, but with excellent achievement in the objective function. This is the highest recommended option among the three evaluated. Efficiency, scalability and accuracy observed in our reservoir management framework makes it highly applicable to near real-time reservoir management in EGS as well as other similar system management processes.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059020","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}

{"title":"Prototype Inversion of Multi-Probe Chemical Sensing Data to Estimate Inter-Well Distributions","authors":"Hsieh Chen, M. Poitzsch","doi":"10.2118/212659-ms","DOIUrl":"https://doi.org/10.2118/212659-ms","url":null,"abstract":"\u0000 For several years, there has been an interest in responsive \"NanoProbes,\" which, when injected along with waterflooding could sense reservoir properties locally along the trajectories they follow from injector to producer wells, giving a low-cost and very deep formation evaluation upon being collected, evaluated, and interpreted with respect to injection point, arrival point, and timings. Here, we introduce these novel \"dual-mode\" NanoProbe tracers, which can undergo chemical transformations when encountering target analytes within the reservoirs.\u0000 We first built the dual-mode chemical sensing tracer functionality into our reservoir simulator and performed forward simulations to acquire model transformed and untransformed tracer breakthroughs. Specifically, the original tracer chemical (denoted tracer-1) can transform into a different chemical (denoted tracer-2) when encountering specific analytes of interest within the reservoir; and the ratio of tracer-1 and tracer-2 from injector-producer pairs provides information about the inter-well analyte distributions. Furthermore, we developed a history matching algorithm based on the iterative ensemble smoother with a rectifier linear unit transformation (ES-MDA-ReLU) that can successfully interpret the inter-well analyte distributions from the chemical sensing tracer data.\u0000 We found that traditional ES-MDA algorithm is ineffective for the history matching of the inter-well analyte distributions form the chemical sensing tracer data if the inter-well analyte distributions are discrete; nevertheless, applying a ReLU filter to the analyte distributions combining with ES-MDA algorithm results in greatly improved history matching results. We also studied the spatial and temporal resolution of the inter-well analyte distributions inverted from the barcoded chemical sensing tracer data, whereby we found that the spatial resolution is sensitive to well spacing as well as the tracer travel paths; and the temporal resolution is sensitive to the shapes of the tracer breakthrough curves (notably, good history matching can already be achieved if the early parts of the breakthrough curves are collected from all producers). Finally, we compared the application of chemical sensing tracers on synthetic reservoir models with homogeneous or heterogeneous permeability fields and found that better history matching can be achieved on heterogeneous fields due to the more diverse travel paths of the chemical sensing tracers.\u0000 Even though the responsive NanoProbes concept has been found promising, the details of the NanoProbes’ working principles and data processing have yet to be fully developed. We believe this work will bridge these gaps and begin to demonstrate the NanoProbes’ potential as novel formation evaluation tools with direct-sensing, low-cost, and very deep reservoir characterization capabilities.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712035","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}

{"title":"Asphaltene Flow Assurance Pre-Risk Evaluation Case Study to Establish Guidelines for CCUS in Arabian Gulf Carbonate Oil Field","authors":"M. Tange, T. Hiraiwa, K. T. Khlaifi, R. Sakurai, S. Bahri, A. Abed, H. Uematsu, K. Makishima, Y. Inokuma, M. Sawata, S. H. Alkaabi, H. Yonebayashi","doi":"10.2118/212648-ms","DOIUrl":"https://doi.org/10.2118/212648-ms","url":null,"abstract":"\u0000 Carbon dioxide capture, utilization and storage (CCUS) has been recognized as a key technology to reduce CO2 emission. Among various CCUS technologies, CO2 enhanced oil recovery (EOR) has been widely implemented at an industrial scale in the E&P sector. However, it is well-known that CO2-mixed oil would cause asphaltene precipitation resulting in flow assurance troubles. Therefore, more advanced asphaltene-risk-managing technology can be an enabler to improve robustness of CCUS projects.\u0000 This paper presents a case study for a comprehensive series of asphaltene flow assurance pre-risk evaluation in Arabian Gulf Carbonate Oil Field at where the CO2 EOR is recognized as one of the highest potential technologies for full-field implementation. At first, sampling location was carefully selected considering the target reservoir's feature because the reliability of asphaltene study highly depends on sample representativeness. After the QA/QC of collected sample, asphaltene onset pressures (AOP) were measured at multiple temperatures under the CO2 mixing conditions in a straightforward experimental-design optimizing manner so that not only the evaluation accuracy could be improved but also the experimental cost could be minimized. The AOP measurements showed clear potential risks associated with CO2 injection. Subsequently, the numerical model analysis was conducted with Cubic-Plus-Association (CPA) EoS model to identify the risk area during CO2 injection. The analysis suggested that a risk would be caused at not only near-wellbore region at the sampling location but also tubing section / surface facility, furthermore, more seriously at the deeper location of target reservoir. Finally, CO2-induced asphaltene formation damage risk was investigated from the viewpoints of precipitated asphaltene particle size and pore throat size in the porous media. As a result, the clogging risks by CO2-induced asphaltene were estimated high in the target reservoir.\u0000 By virtue of the above comprehensive series of pre-risk evaluation, the asphaltene flow assurance risk associated with CO2 injection was identified field-widely. The evaluation findings suggested moving on to future actions such as more detailed formation damage risk evaluation and mitigation plan development. The phased approach for evaluating asphaltene flow assurance risk and the reverse engineering of sampling operational design from the experimental design made a worthy demonstration to reduce unnecessary cost and time while obtaining the key information to drive the project. The procedure in this work can contribute to establish a subsurface part of guideline for CCUS from viewpoints of asphaltene flow assurance risk evaluation.","PeriodicalId":215106,"journal":{"name":"Day 2 Wed, January 25, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128780998","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}