Day 2 Tue, November 01, 2022最新文献

{"title":"Extending the Performance of Standalone Sand Screens (SAS) with Flow Segmentizers in Gas Wells","authors":"S. Shaffee, Hasyimah Ghazali, P. K. Rajan, Wan Amni Wan Mohamad, Maung Maung Myo Thant, M. F. Bakar","doi":"10.2118/211572-ms","DOIUrl":"https://doi.org/10.2118/211572-ms","url":null,"abstract":"\u0000 Sand control application in gas wells is very challenging, especially in the application of a standalone sand screen (SAS) due to the high erosional risks. Many failures have been observed in the industry over the years causing production deferments and additional OPEX to the operators for remedial sand control operations. This work presents the performance evaluation of a unique SAS in open hole completion concept piloted in a horizontal gas well and the replication in other new wells in a Malaysian gas field.\u0000 In 2012, a pilot gas well was completed with SAS with optimally placed flow segmentizers along the horizontal completion to limit the screen erosional risks. The placement was determined using a tool developed through an R&D. It estimates the optimum locations of the flow segmentizers based on the targeted SAS life or erosional velocity limit imposed. At the heart of it is a proprietary erosion model specifically developed for SAS application. The well performance was compared to adjacent wells producing from the same reservoir but completed using the conventional open-hole gravel pack.\u0000 The pilot well achieved higher Productivity Index in comparison to the adjacent wells. Over the 10-year observation period, the production performance was consistent with minimal skin values and no sand production issues. Multifinger Imaging Tool (MIT) was run to measure the erosion levels in the tubing and the result indicated very minimal erosion because of sand production even after several years of production. Recently, another one (1) new infill well was drilled and completed with the same concept as the pilot well. The segmentizer placements were supported by an optimization study based on the expected production scenario. Positive flow back results with no indication of sand production was detected from the intrusive sand monitoring equipment. With the application of SAS and flow segmentizers, a cost reduction of 25% as compared to more complex application of open-hole gravel pack was realized.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124150300","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":"Delineation of Multi-Phase Wellbore and Formation Fluid Distribution in Abu Dhabi's Onshore Horizontal Wells","authors":"Yonghwee Kim, Ahmed Saber Abdel Aziz","doi":"10.2118/211647-ms","DOIUrl":"https://doi.org/10.2118/211647-ms","url":null,"abstract":"\u0000 Abu Dhabi's onshore oil fields are generally mature and contain carbonate reservoirs with light oil and gas. Various well types have been employed, such as vertical, highly deviated, and horizontal wells with cased hole or openhole completions. Surveillance of wellbore and formation fluid attributes is essential for reservoir management decisions and hydrocarbon production enhancement. This paper discusses surveillance techniques to understand multi-phase flow characteristics and reservoir saturation.\u0000 Formation saturation monitoring and production profiling have been performed actively using pulsed neutron logging (PNL) and array production logging (APL) techniques. Challenges in employing the APL method in Abu Dhabi onshore fields include (1) many wells are horizontal with barefoot completions, (2) horizontal sections extend typically more than 2000-ft long with irregular and undulating trajectories, and (3) wells contain asphaltene, debris, and other materials preventing optimal spinner flowmeter functionality. After reviewing each well condition, well environment-specific combinations of APL, nuclear production logging applications for holdups and water velocity calculations, and an advanced three-phase formation saturation analysis technique were determined. This approach overcame several challenges to deliver surveillance objectives.\u0000 We demonstrate four case examples, delineating well-based production and formation saturation profiles in various conditions. Two nuclear measurements exhibiting different sensitivities to oil and gas were combined to compute three-phase formation saturation. When a horizontal openhole wellbore was severely under-gauged, the pulsed neutron-based holdup application was used to avoid an APL tool deployment that might result in tool damage and unsatisfactory data acquisition. Additionally, for wells with good wellbore conditions, pulsed neutron-based and APL-based holdup data sets were acquired, and analysis results were compared. A stationary water velocity calculation method when water cut was high was also adopted to identify downhole water sources, and in-situ water production profiles from APL and nuclear applications were compared.\u0000 An effort to evaluate production profile and in-situ saturation effectively from highly deviated- and horizontal wellbores to improve hydrocarbon production is described. The delineation of production profiles and formation fluid distribution allowed operators to determine reservoir and production management strategies.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126356956","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":"A Machine Learning Based Accelerated Approach to Infer the Breakdown Pressure of the Tight Rocks","authors":"Zeeshan Tariq, B. Yan, Shuyu Sun, Manojkumar Gudala, M. Mahmoud","doi":"10.2118/211129-ms","DOIUrl":"https://doi.org/10.2118/211129-ms","url":null,"abstract":"\u0000 Unconventional oil reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced fractures. To design the hydraulic fracturing jobs, true values of rock breakdown pressure is required. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time consuming process. Therefore, in this study, different machine learning models were efficiently utilized to predict the breakdown pressure of the tight rocks. In the first part of the study, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens, to measure the breakdown pressure. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic cement samples. Rock mechanical properties such as Young's Modulus E, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength sigma_t were measured before conducting hydraulic fracturing tests. Machine learning models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the machine learning model, we considered experimental conditions including injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's Modulus, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength, porosity, permeability, and bulk density. Machine learning models include Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN). During training of ML models, the model hyper-parameters were optimized by grid search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation were predicted with an accuracy of 95%. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127871268","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":"Competitive Commercial and Technical Advantages of Digitization for On-Demand Additive Manufacturing in Oil and Gas Industry","authors":"Mohammed Awadallah, Mustafa Abdelkader, Farah Younis, Yaser Bin Zubaa, Fatima Ahmed Al Zaabi, Ali Al Ali, Adel AL Aidarous, Pedro Carreiras","doi":"10.2118/211803-ms","DOIUrl":"https://doi.org/10.2118/211803-ms","url":null,"abstract":"\u0000 Adapting new technologies in digital transformation within ADNOC is in-line with its 2030 strategy for a smart growth, resulting in maximizing the value of every barrel. 3D printing enables ADNOC to be self-sufficient in sourcing spares locally (especially Business-Continuity). Hence, boosting UAE's economic performance through national investment and leadership.\u0000 Resilient supply chain management is the main contributor towards the success of future organizations. This enables these empower these organizations in terms of performance, profitability, efficiency, and sustainability that will directly ensures business continuity. While effective supply chain is directly dependent on logistics, inventory control, and materials handling, it is also strongly interlinked with the utilization of new technologies that will enhance the complete supply chain cycle.\u0000 This paper demonstrates a hands-on achievements in terms of introducing three-dimensional (3D) printing to ADNOC Group Companies, taking into consideration the challenges that this technology is facing to penetrate the conservative oil and gas industry and realizing its potential benefits to the business. Additionally, the importance and the achievements towards constructing Digital Library in ADNOC specifically and oil and gas companies generally was discussed along with feasibility study which was carried out to evaluate the process from the environmental and techno-commercial perspectives. The technical results exhibited advantage in matching plant's requirement in terms of performance and quality that matches or even exceeds the OEM spares. Whereas commercial advantages with around 50% savings in direct purchases and lead-times, and finally 90% reduction in carbon footprint. Furthermore, a forecasted consumption and saving within all ADNOC Group Companies was extrapolated which shows AED2.1billion worth of products will be made in UAE by 2030 once this technology adopted down the line.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127738678","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":"Integrated Reservoir, Geology and Geomechanical Characterization for Unconventional Development: Application to the Diyab Play","authors":"H. Pourpak, Eider Hernandez, Maxime Higelin, M. Jaber, K. Mansoor, L. Sullivan, E. Baud, K. Su, Muhammad Zeeshan Baig, Hassan Al Marzooqi, Pierre Van Laer, Amena Alharthi, Abdullah Al Hashmi, Trevor Brooks, Mohamed Elsayed Al Arbai","doi":"10.2118/211030-ms","DOIUrl":"https://doi.org/10.2118/211030-ms","url":null,"abstract":"\u0000 The Diyab Formation is an organic-rich carbonate rock with low permeabilities and is one of the first unconventional targets to emerge in the Middle East. Vertical and horizontal exploration wells were drilled during the past years with proven productivity in the United Arab Emirates (UAE). Coupled geomechanical and reservoir characterizations of the Diyab formation are crucial for the successfulness of Stimulated Rock Volume (SRV) Creation and hydraulic fracturing operations which can have a direct impact on production performance. The objective of this study was to perform a full characterization of the Diyab formation based on extensive datasets that include logs and cores. The outcome of this integrated characterization work is used to assess the behavior of the Diyab formation across the concession block.\u0000 First, we present the geology and general context of the studied area. Next, we detail the current understanding of the structural lineaments and natural fractures across the block. Then, based on full characterization work originating from data acquired on exploration and appraisal wells, we show how the results of geomechanical characterization together with the analysis of reservoirs quality/geological data allow us to suggest a vertical sub-division for Diyab formation. We explain further how the reservoir/geology, geomechanical parameters and natural fractures change laterally between wells.\u0000 Reservoir characterization work concluded that there are some lateral variabilities in Diyab formation such as the change in the thickness/mineralogy of the carbonate bench and thickness of the porous wackestone. Some lateral variations in geomechanical/SRV parameters are observed within the block, resulted mainly from change in natural fractures density and properties of the carbonate bench and porous wackestone. This work is the first result of the integration of the current available data and the knowledge on Diyab formation, which could potentially evolve with the acquisition of new data and analyses.\u0000 The combination of a full geomechanical characterization with a reservoir quality and structural geology study allows to propose a detailed reservoir and geomechanical sub-division for the Diyab formation. This approach will aid to better understand the lateral variability of facies, reservoir quality and geomechanical properties within the block which are crucial for successful development of this unconventional play.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116823418","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":"Prediction of Dew Point Pressure for High-Pressure Gas Reservoirs Using Artificial Intelligence Techniques","authors":"Amjed Hassan, M. Mahmoud, A. Abdulraheem","doi":"10.2118/211064-ms","DOIUrl":"https://doi.org/10.2118/211064-ms","url":null,"abstract":"\u0000 Dew point pressure is a curial parameter in characterizing gas reservoirs. Several methods can be used to determine the dew point pressure, including laboratory measurements and empirical models. However, laboratory determinations are expensive and time-consuming, especially for studying high-pressure tight reservoirs where more caution and procedures will be required. While empirical correlations do not accurately reflect the complexity of fluid behavior, and limited models were developed for high-pressure reservoirs. The goal of this work is to develop a reliable tool for predicting the dew point pressure for tight and high-pressure gas reservoirs.\u0000 This work was carried out using five main phases; data collection, quality control, model construction, development of new correlation, and model validation. The data used in this work were obtained based on 250 laboratory measurements. All data were evaluated and the noises and outliers were removed. Different types of artificial intelligence methods were examined to come up with the best determination model. Artificial neural network (ANN) technique, support vector machine (SVM) approach, and adaptive fuzzy logic (AFL) systems were investigated. The hydrocarbon compositions and the molecular weights were used as inputs to estimate the dew point pressure. Different types of error indices were employed to measure the prediction performance of the developed equation. Average percentage error and correlation coefficient values were determined for the different models.\u0000 The developed model predicts the dew point pressure with a percentage error of 4.85% and an R2-value of 0.94. The ANN model developed in this study has 4 neurons and one hidden layer. An empirical equation was proposed based on the best ANN program to provide a direct estimation of the dew point pressure. The extracted equation can provide an average error of 5.74% and an R2-value of 0.93. Overall, the proposed model can reduce the cost and time required for determining the dew point pressure and help to improve reservoir management by providing fast and reliable estimations.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"57 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131540253","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":"A Dynamically Reconciled Digital Twin for Operations Optimization and Decision Support","authors":"P. Thorpe","doi":"10.2118/210987-ms","DOIUrl":"https://doi.org/10.2118/210987-ms","url":null,"abstract":"\u0000 Operational digital twins bring together digitalization technologies, including machine learning, IIoT, data analytics, process simulation and optimization. A digital twin model reconciled with real-time process data provides the foundation for a range of layered applications that are key to transforming the way that process plants and value chains are operated and managed. After outlining the basic numerical methods, this paper will describe three industrial applications of dynamically reconciled digital twin models in gas processing, refining and olefins production. The focus is on application robustness and high availability, delivering increased operating margins and plant flexibility.\u0000 An operational digital twin model can provide a versatile tool for real-time process optimization, and what-if analysis, as well as operations planning and scheduling. To be effective, the digital twin model needs to be reconciled with real-time process data frequently. In a dynamic operating environment where feed composition, process conditions and product demand may change continuously, this challenging data reconciliation task is solved using a combination of numerical methods: Equation-oriented model structures and solvers provide the flexibility required to solve simulation, parameter estimation and optimization cases. Extended Kalman filter and moving horizon estimation enable reconciliation using real-time dynamic data without the need for steady-state operation. Process models are developed using both first principles and data-driven surrogate modelling methods.\u0000 A dynamically reconciled digital twin model provides the basis for an equation-oriented multi-period optimizer that can be solved for facility-wide steady state optimization, short-range dynamic optimization and long-range planning and scheduling problems within a single unified modelling and optimization framework. The use of hybrid models that include both data-driven and first principles components can significantly reduce the deployment and maintenance effort, the resulting low-order models facilitate model reuse across the different optimization horizons and objectives and enable large-scale optimization problems to be solved across an entire process plant or value chain.\u0000 Three industrial applications of these methods will be outlined. The first is a gas plant optimizer that provides real-time process optimization and what-if analysis. The reconciled gas plant model tracks the process under significant feed and ambient variations. The second application is a refinery-wide optimizer that encompasses multiple crude distillation, vacuum and reaction units. The third application is an olefins process scheduling application that optimizes feed distribution, cracking furnace operating conditions and decoke cycle over an extended future time horizon.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128096387","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":"Deployment of a Novel Soft Sensor in the Real Time Optimizer Architecture to Enhance the Integrity and Energy Efficiency of Sulfur Recovery Units","authors":"Satyadileep Dara, I. Khan, Eisa Salem Al Jenaibi, Subhendu Sengupta, Vincent Goveas, Nawal Al Yahyaee, S. Ibrahim, A. Jagannath, A. Raj","doi":"10.2118/211303-ms","DOIUrl":"https://doi.org/10.2118/211303-ms","url":null,"abstract":"\u0000 Commercial analyzers for measuring the aromatics in the Claus furnace exit gas are currently not available and this leads to sub-optimal energy efficiency and poses asset integrity concerns. To address this problem a high-fidelity model is developed to function as a real time analyzer. Objective of this work is to incorporate the soft sensor in the architecture of Real Time Optimizer (RTO) to monitor the presence of aromatics in the Claus furnace exit stream.\u0000 The soft sensor is incorporated in the RTO server which provides the access to the plant operating data and the DCS (Distributed Control System). Soft sensor function in the RTO involves the following steps:\u0000 Soft sensor accesses the plant data and collects the needful input data for simulation Simulation software available in the RTO executes the softs sensor model simulation and generates the aromatics composition data Aromatics composition data is written to the DCS interface as a soft measurement Operators monitor the aromatic composition and accordingly adjust the fuel gas firing\u0000 Aromatic soft sensor is developed as a kinetic model, which is function of rate parameters of several key reactions of the Claus furnace. The kinetic model of the Claus furnace is incorporated in a process simulation model and catalytic convertors are simulated too. Model is validated with large plant data to show that model predicts furnace temperature within +/- 5% error and aromatics composition within +/- 5 ppm.\u0000 Simulation analysis shows that the furnace temperature can be decreased by at least 5 °C while ensuring no BTEX slip. Such change in furnace temperature leads to a reduction in fuel gas flow by ~200 Nm3/h, which translates to a monetary benefit of 0.5 million $/yr. Deployment of the soft sensor is currently in progress through engagement with RTO licensor.\u0000 To the best knowledge of authors, currently, there is no simulator in the market which can adequately model aromatics oxidation phenomena and predict the aromatic content in the furnace exit. This soft sensor being deployed is novel and first of its kind and expected to achieve a sustainable energy efficiency.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"53 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128328117","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":"A Quantum Gravity AI Framework for CO2 Storage Monitoring and Optimization","authors":"Klemens Katterbauer, Abdallah Al Shehri, Abdulaziz Al Qasim","doi":"10.2118/210841-ms","DOIUrl":"https://doi.org/10.2118/210841-ms","url":null,"abstract":"\u0000 Gravimetry is a physical method with a large depth of investigation. Traditional applications include surface gravity observations for mining and oil exploration and borehole gravity logging for investigating formation bulk density. Quantum gravity sensors have recently been developed allowing to achieve considerably higher accuracy and signal to noise ratios as compared to conventional gravimetric approaches. Borehole gravity data have some advantages over the surface data, because the sensors are closer to the reservoir better spatial resolution is obtained; and because the deep borehole gravity data are less affected than surface data by near surface changes.\u0000 We have developed a new AI driven framework for the interpretation and monitoring of CO2 migration for CO2 storage applications. The framework utilize an integrated LSTM -Bayesian inference framework approach that to determine the gravity gradient within the reservoir and infer from this the possible movement in the reservoir. The LSTM framework evaluates the time lapse gravity gradient changes to infer from it the migration of the CO2 movement.\u0000 We evaluated the framework on a public benchmark dataset of the Pohokura field in New Zealand. The Pohokura field in New Zealand has been investigated as a reservoir for CO2 storage given its acceptable reservoir quality and seal rock structure. The framework was evaluated on simulated CO2 storage migration patterns with multiple scenarios, taking into account the uncertainties that may arise with respect to various potential CO2 migration scenarios. The study outlines the enhanced accuracy and tracking of CO2 front movement within the reservoir based on quantum gravity sensors integrated with an AI framework.\u0000 The deep learning framework represents an important step at utilizing quantum borehole gravity sensing for CO2 movement monitoring and the optimization of CO2 storage. The AI framework outlined the considerable potential of quantum gravity sensing for CO2 storage monitoring and optimization.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134299009","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":"Addressing the Challenges of Simulating EOR and CO2 Storage Projects at Reservoir Scale: Accurate Estimation of Trapped Gas Saturation","authors":"S. Aghabozorgi, M. Sohrabi","doi":"10.2118/211366-ms","DOIUrl":"https://doi.org/10.2118/211366-ms","url":null,"abstract":"\u0000 One of the first and foremost steps in the feasibility analysis and site selection of geological CO2 storage projects is estimating the storage capacity of the appointed aquifer or depleted reservoir. It has been established that the volume of CO2 stored due to capillary trapping is significantly higher than other active mechanisms. Therefore, an accurate method is required to determine the trapped gas saturation in the system. This method is also of significant importance for simulating any other process involving cyclic injection of fluids in subsurface reservoirs as the hysteresis in relative permeability is a direct function of trapped gas saturation. Examples of the cyclic process in subsurface reservoir engineering are gas storage projects and reservoirs undergoing Enhanced Oil Recovery (EOR) injections. In this study, we present a detailed study of the reservoir scale simulation results using commercial software, and we discuss the challenges observed in calculating the trapped gas saturation.\u0000 The first challenge is that Land's formulation relates the initial and residual non-wetting saturations measured during an imbibition cycle. However, in many reservoir blocks, the volume and rate of displacing fluid are insufficient to ensure reaching the residual values. Accurate determination of saturation histories in various reservoir grid blocks is also challenging as small oscillations make it hard to identify the flow reversal points. A significant amount of error is introduced in compositional simulations when the composition of trapped gas saturation enters mass transfer calculations, and the trapped gas is dissolved again in the oil phase. Whereas physically, it should be isolated and shielded by the water phase. Finally, an inaccurate definition of saturation-dependent functions can increase the error associated with calculating relative permeability data using trapped gas saturation.\u0000 In this study, we present a new workflow for calculating the trapped gas saturation, addressing all the abovementioned issues. The backbone of this workflow is an efficient algorithm which removes any oscillation misidentified as a flow reversal point. The results discussed in this paper indicate that the available formulation in the literature should be deployed carefully (considering the active mechanism in the system) to decrease the uncertainties. As a result, the feasibility of EOR methods, the site selection for CO2 storage projects and the decision-making process can be based on more reliable data.","PeriodicalId":249690,"journal":{"name":"Day 2 Tue, November 01, 2022","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133218382","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}