Day 2 Tue, May 23, 2023最新文献

{"title":"Prediction of Surface Wellhead Deformation Via Calculation of Tubular Growth Due to Thermal Effects","authors":"Manuel Alejandro Proano, A. Meneses, Victor Andres Tates, J. Aponte, T. Magalhaes, Gerardo Javier Duque","doi":"10.2118/212960-ms","DOIUrl":"https://doi.org/10.2118/212960-ms","url":null,"abstract":"\u0000 After implementing a water injection project with high temperature (up to 235°F @ surface) sourced water in existing wells, 63% of the conventional Wellhead associated with waterflooding installed showed growth/inclination at the surface. A detailed analysis of twenty-seven wells in the Orient Basin of Ecuador was performed, where onsite measurements and engineering calculations of the loads and elongations of the production casing and tubing strings were caused by thermal effects.\u0000 A specialized tubular modeling software was used to simulate the water producer wells and injector well thermal profiles, casing elongation, and buckling. The produced water has temperatures superior to 200°F and is transferred directly to the injector wells without any allowed time to cool down. The analysis focused mainly on the 9-5/8″ production casing since it is not cemented to the surface and has almost 6,000ft of free pipe able to elongate due to high-temperature injected water. As the other casing strings were cemented to the surface, it was assumed that they were adequately anchored, and no elongation would occur.\u0000 All the wells were inspected before entering service, and no wellhead deformation was observed. Once operations started, the surface temperatures reached up to 235°F on the water producers and injectors. After six months, the production operators noticed the first signs of wellhead movement, with consequent inclination. A survey was carried out in 27 wells, where the wellhead deformations were measured by taking the projection in 3 planes (X, Y, and Z). Out of 27 wells surveyed, two water producers presented an inclination above 2° and one injector well above 1.5°, which was deemed excessive. It was possible to establish a good correlation between the specialized tubular modeling software results and the deformations measured at the surface, enabling the development of guidelines to define if high-temperature wellheads with expansion capabilities would be required.\u0000 It also evaluated the possibility of using insulated packer fluids to minimize heat transfer and production casing elongation. The simulation estimated an average elongation reduction of 28% and 41% when water-based and oil-based packer fluids are used, respectively.\u0000 This work created a workflow to identify wells with a high risk of suffering wellhead deformation caused by high surface temperature. Combining the simulation results with the field measurements made it possible to determine when deformation at the surface was expected and high-temperature wellheads with expansion capabilities were needed. It also evaluated the effectiveness of insulated packer fluids in reducing the casing elongation and avoiding the installation of such wellheads.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130374774","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 Single Proppant Terminal Settling Velocity in High Viscosity Friction Reducers by Using Artificial Neural Networks and XGBoost","authors":"Xiaojing Ge, Rong Lu, Ghith Biheri, Abdulmohsin Imqam, B. Bai","doi":"10.2118/212964-ms","DOIUrl":"https://doi.org/10.2118/212964-ms","url":null,"abstract":"\u0000 High viscosity friction reducers (HVFRs) have been recently gaining more attention and increasing in use, not only as friction-reducing agents but also as proppant carriers. The settling velocity of the proppant is one of the key outputs to describe their proppant transport capability. However, it is influenced by many factors such as fluid properties, proppant properties, and fracture properties. Many empirical/physics-based models and correlations to predict particle settling velocity have been developed. However, they are usually based on certain assumptions and have applicable limits. In contrast, machine learning models can be considered as a black box. The objective of this study is to use machine learning models to find the relationship between the multiple factors mentioned above and particle settling velocity in order to correctly predict it. Two of the most popular and powerful machine learning algorithms, Artificial neural networks (ANN) and XGBoost, were comparatively investigated with standard data processing and training procedures. Mean Absolute Errors (MAEs) for ANNs and XGBoost were 0.010379 and 0.004253 respectively. The XGBoost learning algorithm had overall better prediction performance than the ANN model in terms of the data sets used for this study and had the potential to properly handle missing values by itself.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126272749","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":"Spatial Data Assimilation and Risk Forecasting in Geologic CO2 Sequestration","authors":"Bailian Chen, Q. Kang, R. Pawar","doi":"10.2118/212975-ms","DOIUrl":"https://doi.org/10.2118/212975-ms","url":null,"abstract":"\u0000 Geologic uncertainty usually leads to large uncertainty in the predictions of risk-related system properties and/or risk metrics (e.g., CO2 plumes and CO2/brine leakage rates) at a geologic CO2 storage site. Different type of data, e.g., point measurements from monitoring wells and spatial data from 4D seismic surveys, can be leveraged or assimilated to reduce the risk predictions. In this work, we develop a novel framework for spatial data assimilation and risk forecasting. Under the U.S. Department of Energy's National Risk Assessment Partnership (NRAP), we have developed a framework using an ensemble-based data assimilation approach called ES-MDA-GEO for spatial data assimilation and forecasting. In particular, we took CO2 saturation maps (can be interpreted from 4D seismic surveys) as inputs for spatial data assimilation. Three seismic surveys at years 1, 3 and 5 were considered in this study. Accordingly, three saturation maps were generated for data assimilation. The impact of level of data noise was also investigated in this work. Our results show increased similarity between the updated reservoir models and the \"ground-truth\" model with the increased number of seismic surveys. Predictive accuracy in CO2 saturation plume increases with the increased number of seismic surveys as well. We also observed that with the increase of the level of data noise from 1% to 10%, the difference between the updated models and the ground truth does not increase significantly. Similar observation was made for the prediction of CO2 plume distribution at the end of post CO2 injection period by increasing the data noise. Our study results also indicate that comparatively spatial data carry more value than point measurements from monitoring wells to reduce the uncertainty in the risk predictions.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121398697","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":"Forecasting Oil Recovery Using Long Short Term Memory Neural Machine Learning Technique","authors":"Jonathan Asante, W. Ampomah, Martha Carther","doi":"10.2118/212967-ms","DOIUrl":"https://doi.org/10.2118/212967-ms","url":null,"abstract":"\u0000 This paper focuses on using a timeseries neural network to forecast the oil recovery of a mature oil reservoir undergoing tertiary CO2 water alternating gas (WAG) enhanced oil recovery (EOR). Estimating future oil recovery is a necessity for planning an effective EOR strategy. Because of the high uncertainty associated with numerical modeling input parameters, modeling is not necessarily an accurate predictor of future performance for a specific well or even an entire field. The evolution of machine learning algorithms has shown that data-driven models can make decisions based on trends and pattern recognition to achieve tractable, robust, and cost-effective solutions.\u0000 The methodology is validated by analyzing a five-spot pattern from the study field. The one injector well and four producers within the pattern are considered to be mutually connected. The multivariate timeseries (MTS) field data utilized in the model construction include production bottom-hole pressure, injection pressure, WAG cycles, and injection volumes. These MTS input data were preprocessed into a format that is more understandable and useful for the model. A Long-Short-Term Memory (LSTM) neural network model was established to determine patterns and trends, discover relationships from MTS data, and subsequently predict oil recovery through model-fitting. During the model construction, the preprocessed dataset was split into training and testing sets based on production time periods. The largest portion of the data set is apportioned to train the model, and it also corresponds to the earliest part of the production. The model is tested on the remaining data set chronologically.\u0000 Analysis of field history calibration through loss iteration of the training dataset shows a low mean squared error of 7.16 and a relatively high R-squared value of 0.92. The developed model was validated using a test set, and results showed high-level model predictability of an R-squared value of 0.88. Additional model validation was performed using other wells’ information within the pattern as a blind test dataset. An average R-squared of 0.88 was observed for the other producing wells. The validated model was used to forecast oil recovery into the future with a reasonable outcome. From the forecast, uncertainty increased with the increasing length of time in the future, and the alteration of the WAG cycle significantly impacted the oil recovery. The LSTM model can predict oil recovery with a high level of accuracy. The successful predictions and reasonable forecasting of the oil recovery prove the effectiveness and usefulness of data-driven models.\u0000 The workflow presented in this paper predicts the oil recovery without a detailed geological model and/or numerical simulation; it only considers time-changing parameters. Analyzing the LSTM model's results provides robust guidance to adjust real-time field development plans.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131024770","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":"Improved Model for Predicting the Productivity of Multi-Fractured Shale Wells. TMS and EFS Field Data as Case Studies","authors":"A. Fadairo, S. Egenhoff, G. Adeyemi, Kegang Ling, O. Tomomewo, Adebowale David Oladepo, Opeyemi Oni, Richmond Nduka Nwaokwu","doi":"10.2118/212947-ms","DOIUrl":"https://doi.org/10.2118/212947-ms","url":null,"abstract":"\u0000 Multi-fractured horizontal wells have been an admirable completion technique for unconventional resources such as in Tuscaloosa Marine shale (TMS) and Eagle Ford Shale (EFS) plays located in the United States. Studies have shown that the productivity of multi-fractured wells of these two shale plays are majorly based on the fracture conductivity, which may be dependent on the type of the geometrical shape of the fractures connecting the fluid to the well. A reliable model is desirable to the operator to accurately capture the productivity of multi-fractured shale wells. Several mathematical models have been adopted with various assumptions that include simple slot geometry for fracture shape in the derivation of production rate models. These assumptions significantly simplify the existing model's applications but limit the efficiency of the models to accurately predict the fluid production rate.\u0000 Failure to utilize an elliptical fracture shape and a correct drive mechanism-based model for analyzing flow rate have been considered as a vital reason for the disparity between the calculated results by the past investigators and the exact values obtained from TMS and EFS field measurements. In this study, an elliptical model based on the fracture geometry has been derived to analyze the productivity of multi-fractured shale wells considering the accurate drive mechanism for the shale play. The model validation has been achieved using field data from the Tuscaloosa Marine shale (TMS) and the Eagle Ford Shale (EFS) plays. The results generated from the newly improved model resulted in more accurate outcomes when compared with results presented by Yang and Guo (2019) and Guo and Schechter (1997); all these authors assumed the cross-sectional area of the induced fractures as being a slot showed nonconformity using real life values from the Tuscaloosa Marine shale (TMS) and the Eagle Ford Shale (EFS) plays as benchmarks. The newly improved model reduces the prediction percentage error to 0.55% and 0.43% compared to the percentage error reported by Yang and Guo (2019) as 9.1% and 3.5% and by Guo and Schechter (1997) respectively as 29.7% and 47.2 % using the actual oilfield results as their benchmark. The accurate prediction of the long-term productivity of multi-fractured oil shale depends on the ability to determine fracture geometry and the drive mechanisms that dominantly control flow in the shale play considered. Sample calculations of flow rate of the two fields considered and the controllable parameters influencing the flow rate have also been identified. The study would serve as a tool for accurate assessment of flow rate in multi-fractured wells of shale plays and analyzes its performance.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131439859","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":"High Resolution Modeling of Pore Pressure Change, Fault Slip Potential and Induced Seismicity in the Fort Worth Basin","authors":"Changqing Yao, Jaeyoung Park, Hongquan Chen, A. Datta-Gupta, P. Hennings, R. Dommisse","doi":"10.2118/212951-ms","DOIUrl":"https://doi.org/10.2118/212951-ms","url":null,"abstract":"\u0000 The seismicity rate in the Fort-Worth Basin, north-central Texas, increased significantly from 2008. Previous studies attributed the seismic events to pore pressure increase caused by wastewater injection into the Ellenburger Formation. However, hydrocarbon production from the overlying Barnett Shale was not included in those previous studies. This study introduces a basin wide 10 year modeling and analysis of pore pressure change, fault slip potential and regional seismic events in the Fort-Worth Basin considering all production and injection activities.\u0000 In order to better understand the controlling mechanisms of induced seismicity and its relationship to hydrocarbon production and water disposal, a 3D heterogeneous basin model, which incorporates all available well log data, stratigraphic data from more than 1200 wells and petrophysical analysis of 47 wells is constructed. The high resolution 90 million cell model is first upscaled using a combination of layer and areal coarsening scheme to improve simulation efficiency and then calibrated using a streamline-based inversion method with the bottom-hole pressure of 122 injection wells. The history matching period includes injection volume history and production history of more than 18000 producers for over 10 years. Next, we performed 3D fluid flow simulation on the calibrated upscaled model from which basin-wide pressure distribution was generated. Finally, fault slip potential of all the interpreted faults are calculated based on pore pressure results and fault framework model to analyze the controlling mechanisms of seismic events in the Fort Worth Basin.\u0000 There are five main seismicity sequences in the Fort Worth area, which are DFW Airport Earthquake (2008), Cleburne Earthquake (2010), Azle Earthquake (2013), Irving Earthquake (2014) and Venus Earthquake (2015). Based on the pore pressure results from fluid flow simulation, all earthquake sequences, except for the Azle earthquakes, were found to be triggered by pore pressure increase. We previously proposed a different controlling mechanism for the phenomenon observed in Azle area, which is the unbalanced loading on two sides of a critically stressed fault. The results from this study are consistent with the previous findings. Fault slip potential calculations further validate the correlation between seismic events and pore pressure increase due to wastewater injection. By plotting the cumulative distribution of fault slip potential of all faults in the basin, we could identify the faults which are most likely to slip and trigger earthquakes.\u0000 The novelty of this work is basin-wide identification of mechanisms of induced seismicity considering hydrocarbon production and fluid injection using basin scale high resolution fluid flow modeling and streamline-based inversion of pressure history.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129393368","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":"Optimizing Oil Recovery and CO2 Sequestration in Carbonate Reservoirs using Voidage Replacement and Production Balancing Methods","authors":"Z. Hamdi, Mohammed Ahmed Al-hadrani, M. Bataee","doi":"10.2118/212983-ms","DOIUrl":"https://doi.org/10.2118/212983-ms","url":null,"abstract":"\u0000 Replacement of voidage relates to the method of injecting liquids to replace oil, water and gas inside the reservoir. The technique for voidage replacement is the volume of injected fluids in the barrel over the volume they got produced. Injected liquids may be wasted outside the floodable quantity of the pore in the reservoir during injection, because of many factors such as bad cement in the casing leakage, etc. A Voidage Replacement Ratio of 1.2 is chosen to preserve the replaced fluid pressure of the reservoir. The balancing Production technique relates to the determination of a scheduled rate of production to be accomplished at a particular time. To preserve the reservoir pressure, a production balancing ratio of 1.2 should be selected for a higher production rate that is balanced with the injected fluids. The balancing production approach involves ensuring that the liquids injected fulfil the production ability needed to prevent any reservoir pressure unbalance. The water alternating gas (WAG) method is the technique to improve oil recovery by injecting water to enhance sweep effectiveness. If gas is being injected into this technique, the viscosity of the oil will be reduced, making it convenient for the water to sweep it well into the production wells. WAG-365 case of a 365-day cycling period is the best one to be selected as longer time provides the gas with the opportunity to guarantee two things, sweeping the residual oil that is immobile and trapped inside the tight pores and also dissolving in the oil and reducing its viscosity. CO2 sequestration relates to the subterranean CO2 injection method to prevent the atmosphere from CO2 emissions from burning fossil fuels that subsequently lead to global warming and hazardous climate change. Throughout the WAG method, CO2 is used and injected to be stored as a solution and the quantity of CO2 stored is defined by changing the solubility of the gas. The more the gas's solubility results in a large quantity of stored CO2.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114797899","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":"What Did We Learn from SAGD Applications in Three Decades, and What is Next?","authors":"R. Pratama, T. Babadagli","doi":"10.2118/212970-ms","DOIUrl":"https://doi.org/10.2118/212970-ms","url":null,"abstract":"\u0000 Steam-assisted gravity drainage (SAGD) has proved to be a technically and commercially successful methodology for recovering heavy-oil in Canada. At present, there are 22 commercial SAGD projects with over 300 pads and 2,700 well pairs, contributing to nearly 1.4 million bbl/day of production. The steam growth in the steam chamber could recover up to 60% of the oil-in-place by a typical SAGD project. However, some SAGD projects are only able to present less than 20% of the recovery factor, even though they have been producing for almost decades. Currently, the steam-to-oil ratio (SOR) for most SAGD projects ranges between 2 and 4 bbl steam/bbl oil. Nevertheless, some projects are still experiencing SOR of over 4 bbl/bbl due to the aggressive steam injection.\u0000 Despite the efficacious evidence and enormous contribution to oil production, many questions regarding the current SAGD project performance are still rising. The process and execution are very complex and entail great operational excellence. The thermodynamic processes (heat transfer, wettability alteration), reservoir geology (thickness, vertical conformance, steam channelling), well designs (optimal placement of the pairs, well completions), and environmental concerns (GHG emission) are also limiting factors to be detrimental to SAGD performance. Some other techniques to recuperate heavy-oil and bitumen (e.g., co-injection)—in addition to the principal SAGD—have been insinuated and employed in the projects. The efforts only presented a 5–10% of success rate.\u0000 This paper focuses on extensive evaluation and analysis of the ongoing SAGD projects over the last three decades in Canada and what would be the forthcoming potential of mature SAGD. Lessons learned and limitations from historical and current SAGD applications based on the evaluation of 22 commercial SAGD projects are presented. Success and failure stories were evaluated from geological, technical, environmental, and operational points of view. The reasons behind the successful applications of existing SAGD practices were listed. In the end, suggestions were made as to the proper design of new SAGD projects and future practices in the matured fields.\u0000 Some new insights for the future of mature SAGD, including \"zero emission\" applications using solvents and reduced emission using steam additives, are also discussed. The conclusive analyses done and the recommendations made will lead to more efficient SAGD applications (new and matured) in Canada, also providing a useful road map for the other parts of the world.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132907429","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":"Data-Driven Machine Learning Approach to Predict the Trapping Indices in Coupled Chemo-Hydro-Mechanical Model of CO2 Sequestration into Deep Saline Aquifer","authors":"Zeeshan Tariq, B. Yan, Shuyu Sun","doi":"10.2118/212980-ms","DOIUrl":"https://doi.org/10.2118/212980-ms","url":null,"abstract":"\u0000 Storing carbon dioxide (CO2) in deep geological formations, such as saline aquifers and depleted oil and gas reservoirs, through Geological Carbon Sequestration (GCS) offers tremendous potential for large-scale CO2 storage. However, ensuring the successful implementation of GCS requires a thorough evaluation of the risks associated with confinement of plumes and storage capacity at each storage location. To gain a better understanding of how CO2 is trapped in saline aquifers, it is important to create robust and speedy tools for assessing CO2 trapping efficiency. Therefore, this study focuses on using machine learning techniques to predict the efficiency of CO2 trapping in deep saline formations as part of Geological Carbon Sequestration (GCS). The methodology involves simulating the CO2 trapping mechanisms using a physics-based numerical reservoir simulator and creating training, testing, and validation datasets based on uncertainty variables. The study used a numerical reservoir simulator to simulate CO2 trapping mechanisms over 170 years, with uncertainty variables like petrophysical properties, reservoir physical parameters, and operational decision parameters being utilized to create a large dataset for training, testing, and validation. The study identified key control variables through feature importance index calculation and utilized the Latin-Hypercube approach to account for a wide range of parameters. 722 reservoir simulations were performed and the results of residual trapping, mineral trapping, solubility trapping, and cumulative CO2 injection were analyzed. The outliers and extreme data points were removed using statistical and exploratory data analysis techniques. Deep neural network was applied to predict the CO2 trapping efficiency. The results showed that the deep neural network model can predict the trapping indices with a coefficient of determination above 0.95 and average absolute percentage error below 5%. These findings suggest that machine learning models can serve as a more efficient alternative to traditional numerical simulation for estimating the performance of CO2 trapping in GCS projects.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132568700","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":"Contribution of Wettability Alteration to Imbibition Oil Recovery in Low and High Permeability Conditions","authors":"Xiao Deng, M. Kamal, S. Patil, S. M. Shakil, D. A. Al Shehri, Xianmin Zhou, M. Mahmoud, E. A. Al Shalabi","doi":"10.2118/213045-ms","DOIUrl":"https://doi.org/10.2118/213045-ms","url":null,"abstract":"\u0000 Low permeability rock usually holds a large amount of residual oil after flooding. The two most important mechanisms for residual oil recovery are interfacial tension (IFT) reduction and wettability alteration (WA). There is confusion around the coupled effect between the two mechanisms. Permeability is found to be a critical factor on the coupled effect. In this study, the spontaneous imbibition oil recovery results from core plugs of different permeability by using two surfactants were compared. The comparison helps understand the impact of permeability on the coupled effect of IFT reduction and WA.\u0000 Filtered crude oil (density 0.87 g/mL, viscosity 12.492 cP), Indiana limestone cores of different permeabilities, two locally synthesized cationic gemini surfactants, GS3 and GS6, were used in this study. The spinning drop method and static contact angle method were used to measure the oil/water IFT and the wettability. Spontaneous imbibition experiments using Amott cells were conducted at the ambient condition to relate IFT reduction and WA performance to the oil recovery contribution.\u0000 Results showed that although the selected surfactants had comparable IFT reduction performance, GS3 is much stronger than GS6 in altering oil-wet carbonate rock to water-wet conditions. In core plugs with the same dimensions and comparable low permeabilities, the oil recovery values accorded with the WA performance. GS3 obtained faster and higher oil recovery (24%) than and GS6 (14%), indicating that enhancing WA alone contributes to oil recovery. The main difference between the selected surfactants was the spacer structure. It appeared that introducing unsaturation into the spacer group harmed the WA performance. Comparing different permeability conditions, GS6 obtained much higher oil recovery in a high permeability condition (922 mD) than in a low permeability condition (7.56 mD). Though permeability significantly impacted the whole imbibition process, it was more auspicious when IFT reduction became the main driving force.\u0000 This study studied the WA mechanism alone by adopting surfactants with comparable oil/water IFT values. It also features the impact of permeability by comparing the recovery curve by the same surfactant under different permeability, showing that IFT reduction contributes more to oil recovery in high permeability rock.","PeriodicalId":428381,"journal":{"name":"Day 2 Tue, May 23, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116299378","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}