Day 1 Wed, March 13, 2024最新文献

{"title":"Continuous Feedback Loop Between Surface and Downhole for Autonomously Drilling a Section","authors":"Qiuhua Liu, Samba Ba, Prateek Goel, Jinsoo Kim","doi":"10.2118/218117-ms","DOIUrl":"https://doi.org/10.2118/218117-ms","url":null,"abstract":"\u0000 Advanced AI planner and connected intelligent systems are used to obtain a continuous feedback workflow between surface and downhole. This significantly increases the consistency and efficiency of well construction operations while minimizing human involvement and footprint, advancing efforts towards autonomous operations, and lowering the operational carbon footprints by an order of magnitude.\u0000 To send steering instructions to a rotary steerable system (RSS), one often uses modulations of flow or rotation per minute (RPM) called downlinks. The proposed workflow presented here includes an automated downlink detection and an AI planner to determine the optimum downlinking sequence for executing directional drilling decisions. The automated downlink detection is based on an intelligent system that will continuously monitor pressure and RPM waves at the surface and raise an automation event when it detects downlink patterns that match predefined patterns with a certain confidence level. Once the current steering mode is clearly identified and the geometric trajectory to follow has been generated, the next step is to define the optimum downlinking sequence to follow that trajectory path. This is done by the AI planner, which carefully analyzes the current state, the available downlinking map, and the desired state to derive the most favorable path to the desired state.\u0000 The autonomous workflow has been tested in the field in many different countries across the world. The automated downlink detection located at the surface has an average detection rate of more than 98% with more than 10,000 downlinks to be detected every month across numerous field locations. On rare occasions, where the surface downlink detection is not picking up the downlink signal, the downhole downlink detection will send downlink confirmation from downhole to surface via the measurement-while-drilling telemetry system. This makes the overall system exceptionally robust. In addition, the intelligent downlink sequence optimization will often find the shortest path from the current tool state to the desired state, thus increasing the efficiency of the drilling operations. With the native cloud monitoring system, domain experts can access the system at any time from wherever they can log into the secure robust network.\u0000 The synergy between surface systems and downhole systems has enabled the development of a unique autonomous well construction system that improves performance while reducing the carbon footprint, allowing domain experts from all over the world to remotely monitor their operations without any traveling needed.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140394944","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":"Production Forecast for Multistage Hydraulically Fractured Shale Gas Well Based on Integration of Domain Knowledge and Deep Learning Algorithm","authors":"Yang Luo, Bo Kang, Jianchun Guo, Yan Feng, Liping Jiang, Wei He, Yi Cheng, Yong Xiao, Xing Zhao, Daju Shi, Cong Lu","doi":"10.2118/218105-ms","DOIUrl":"https://doi.org/10.2118/218105-ms","url":null,"abstract":"\u0000 Accurate production forecast of multistage hydraulically fractured wells is crucial for the development of shale gas reservoir. While several data-driven production models have been proposed, few of them take into account the physical mechanism and production process.\u0000 In this paper, we present an innovative approach that combines domain knowledge with a deep learning algorithm to establish an accurate and interpretable production model. Integrated production influencing factors were extracted from four main aspects: geological reserve, fracture network shape, fracture conductivity and production control. Instead of treating the entire horizontal well as an average, features from individual stages were utilized as the model inputs. To account for the sequential gas aggregation from bottom to top stages, the gated recurrent unit (GRU) algorithm was applied, and an additional Mask layer was introduced to accommodate varying stage numbers among different wells.\u0000 The production model was trained with real data from 119 wells in Weiyuan shale gas field, China. The hyper-parameters were optimized using Bayesian optimization method, resulting in a robust performance with an average relative error (MRE) of 11.7%. This MRE is 77.4% lower than that of the traditional multilayer perceptron model. Furthermore, our model outperformed the simple GRU model by 37% in terms of MRE, demonstrating the significance of the Mask layer in avoiding data redundancy and improving information transmission efficiency. The results in this paper indicate the importance of considering input variables at the stage level and highlight the benefits of incorporating domain knowledge into production forecast.\u0000 Unlike conventional models that are solely data-driven, the approach proposed in this paper faithfully aligns with the actual production process of multistage hydraulically fractured wells, leading to significantly improved prediction performance. This study shows the potential of integrating domain knowledge and deep learning algorithm for production forecast in shale gas reservoirs.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"103 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395735","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":"Investigation of Fracture Characterization Through the Transport Behavior of Magnetic Nanoemulsions","authors":"Seyedeh Hannaneh Ahmadi, S. Maaref, Boxin Ding, Seyed Emad Siadatifar, Daniela Mayorga Ariza, Steven L. Bryant, A. Kantzas","doi":"10.2118/218036-ms","DOIUrl":"https://doi.org/10.2118/218036-ms","url":null,"abstract":"\u0000 Hydraulic fracturing is a promising way to increase oil recovery in tight reservoirs. The accurate assessment of the expansion of fracture networks within the reservoir is a prominent challenge encountered in hydraulic fracturing operations. To overcome this obstacle, the incorporation of tracer injection along with the fracturing fluids and flowback analysis provides fundamental knowledge for reservoir characterization. Herein, a magnetic Pickering nanoemulsion is introduced as a potential tracer for hydraulic fracturing applications. In this regard, the transportation behavior of this particular Pickering nanoemulsion is a crucial factor for designing and optimizing its implementation in energy, sustainability, and environmental areas. In this study, a polymer-coated iron oxide (Fe3O4) nanoparticle (NP) was synthesized and utilized as a stabilizer to form stable oil-in-water (O/W) nanoemulsions. Two different types of nanoemulsions (with the highest and the lowest stability) were then fabricated with various ratios of polymer shell to Fe3O4 nanoparticle core (3:1 and 0.5:1). Core flooding experiments were then performed in a sandpack to evaluate the stability and retention behavior of the synthesized nanoemulsions in porous media. The nanoemulsion flow profiles were quantitatively monitored by X-ray CT scanning and sandpack magnetic susceptibility experiments. In addition, the density of the nanoemulsion effluents and pressure drop during the whole flooding process were measured to evaluate the most effective nanoemulsion formulation with the lowest retention in the porous media. According to the obtained results, the most stable nanoemulsion formulation with the highest polymer-to-nanoparticle coating ratio (3:1) was transported through the sandpack effortlessly with the lowest retention. The pressure-drop results also showed a gradual increase of pressure during nanoemulsion flooding due to the drag force and higher viscosity of the nanoemulsions compared to water. However, throughout the chase water flooding, the nanoemulsion with the highest stability was displaced piston-like along the sandpack with lower pressure drop, implying lower retention of the nanoemulsion at the higher ratio of polymer to nanoparticle coating.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"32 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140284351","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":"Exploring Pressure Interactions Between Geological CO2 Storage Projects in Alberta Using Analytical and Numerical Simulations","authors":"Mammad Mirzayev, Sean McCoy, Joanna Cooper, Don Lawton, Liangliang Jiang","doi":"10.2118/218038-ms","DOIUrl":"https://doi.org/10.2118/218038-ms","url":null,"abstract":"\u0000 Deep saline aquifers are considered as attractive hosts for geological CO2 storage (GCS). The Western Canada Sedimentary Basin (WCSB) comprises numerous saline aquifers. Depending on formation geochemistry, in-situ conditions, and petrophysical properties, each formation will have different storage capacity and efficiency. There are currently 25 proposed projects in the province of Alberta that, if they were all approved with pore space tenure, would inject large volumes of CO2 at relatively large rates into the WCSB. The goal of this study is to present characterizations of potential storage formations and examine the potential for interactions between proposed projects in proximity.\u0000 We use numerical and analytical simulation to assess the evolution and interaction of pressure fields from proposed projects over time in the Wabamun Area CO2 Sequestration Project (WASP) region and infer how this affects cumulative storage capacity in the target formations. We compare the result from simple, relatively easy-to-use analytical tools against numerical simulation results to determine whether existing screening tools could accurately capture potential interactions between proximal projects. The results reveal that with increasing length of injection years, the radial extent of the CO2 pressure distribution increases as well. The analytical solution cannot accurately capture formation compressibility and variation in fluid properties, which leads to deviations from the simulated values. Nonetheless, these tools could serve as a quick method to identify areas of concern, and both can provide useful information about the likely extent of the pressure plume in a GCS project.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"64 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285048","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":"Developing A ChatGPT for PDO Standard Specification","authors":"F. K. Al Kalbani, S. Al Faraei, A. Al Mashani","doi":"10.2118/218113-ms","DOIUrl":"https://doi.org/10.2118/218113-ms","url":null,"abstract":"\u0000 The advancement of digital platforms and natural language processing technologies has led to the development of innovative solutions aimed at enhancing access to information. This abstract introduces PDO ChatGPT, a system designed to facilitate streamlined access to international standards, procedures, guidelines, and specifications through a user-friendly interface. Leveraging Streamlit as the frontend and OpenAI's Language Model (LLM) as the backend, PDO ChatGPT offers users an interactive platform where they can upload PDF specification files. Upon upload, the system extracts text content, segments it into manageable chunks, and employs embedding techniques to create a semantic index within a knowledge base. When users raise inquiries through the Streamlit portal, their queries undergo embedding and initiate a search in the semantic index. The system retrieves the most relevant embedded chunks based on similarity scores, compiles coherent responses, and presents them within the Streamlit chat interface. This methodology combines PDF processing, semantic indexing, and real-time query processing to deliver immediate and accurate information tailored to user queries. PDO ChatGPT stands as an innovative tool bridging the gap between complex documentation and accessible, on-demand knowledge retrieval for users seeking specific information from extensive standards and guidelines.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"73 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285139","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 New Approach of Predicting Fractured Tight Sandstone Reservoir Permeability Based on Electrical Image Logging","authors":"Gao-ren Li, Ronghui Yan, Tianding Liu, Xiaoping Sun, Jiaqi Li, Liang Xiao","doi":"10.2118/218025-ms","DOIUrl":"https://doi.org/10.2118/218025-ms","url":null,"abstract":"\u0000 Permeability is an important input parameter in Tight reservoir characterization and evaluation, precisely predicting formation permeability is indispensable. However, permeability prediction faces great challenge in tight sandstone reservoirs, empirical statistical methods, and nuclear magnetic resonance (NMR) based models lose their role due to complicated pore structure and the effect of methane gas (CH4) or hydrocarbon to NMR responses. In addition, fractures also play important role in improving tight reservoir permeability, whereas current logging responses cannot be used to characterize this improvement besides electrical image logging. In this study, to quantitatively characterize the improvement of fractures to filtration capacity in tight sandstone reservoir and accurately predict permeability, the Triassic Chang 63 Member of Jiyuan Region, Northwestern Ordos Basin is used as an example, a novel model of predicting permeability from electrical image logging is raised. In this model, the porosity frequency spectra are first extracted to characterize the pore structure of fractured tight sandstones. Afterwards, two parameters, which are defined as the logarithmic geometric mean value (φmv) and the golden section point variance (σg) of porosity frequency spectrum, are extracted to characterize the contribution of fractures to permeability. Comparing with the shape of porosity frequency spectrum, permeability φmv and σg, the quality of our target fractured tight sandstone reservoirs is quantified, and relationships among permeability, φmv and σg are established. High-quality reservoirs exhibit wide porosity frequency spectrum, high values of φmv and σg, and vice versa. Three parameters, which are formation total porosity, φmv and σg, are chosen to establish a novel fractured tight sandstone reservoir permeability prediction model. The involved input parameters in this model are calibrated by using the routine experiments of 35 core samples. Finally, we apply this model into field applications to consecutively calculate permeability in the intervals with which electrical image logging is acquired. Comparison of predicting permeability with core derived results illustrate that our raised model is usable in the Chang 63 Member of Jiyuan Region. The average relative errors between these two kinds of permeabilities is only 16.54% in 12 wells. This research gives a novel technique of calculating permeability in fractured tight reservoirs. It can avoid the effect of CH4 or hydrocarbon on conventional and NMR logging responses, and will play a great important role in unconventional reservoirs permeability prediction and formation characterization.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"10 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395273","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":"Investigating the Effect of Capillary Pressure on DFIT and DFIT-FBA Analysis","authors":"S. Haqparast, D. Zeinabady, C. R. Clarkson","doi":"10.2118/218067-ms","DOIUrl":"https://doi.org/10.2118/218067-ms","url":null,"abstract":"\u0000 The diagnostic fracture injection test (DFIT), and the new variation DFIT-FBA (FBA = flowback analysis), are well-testing methods performed before the main hydraulic fracturing operations to obtain several key parameters used in hydraulic fracture design and for reservoir simulation input. The classic (conventional) DFIT includes the sequence of pump-in, followed by a long shut-in (hours to days), while DFIT-FBA utilizes the sequence of pump-in, followed by a brief (minutes) shut-in, and then flowback to accelerate the time to reach closure and obtain reservoir pressure. While DFITs are still widely implemented, DFIT-FBA has the advantage that key properties can be derived in a matter of 2-3 hours versus (typically) more than a day for a conventional DFIT.\u0000 The current models used to estimate reservoir parameters from DFIT and DFIT-FBA require calculating the fracturing fluid leakoff volume into the reservoir. While mechanisms affecting leakoff include viscous, capillary, and osmotic forces, current DFIT models only consider viscous forces. While the effect of the capillary pressure on fluid leakoff has been explored and confirmed by multiple researchers, it has not been incorporated into models and software for hydraulic fracture modelling or DFIT/DFIT-FBA analysis.\u0000 An important question addressed in this work is whether the capillary pressure effect is significant over the short timeframe of the DFIT/DFIT-FBA test. Simulation results generated herein demonstrate that capillary pressure plays a significant role in the leakoff of hydraulic fracturing fluid into the reservoir during DFIT/DFIT-FBA tests; therefore, neglecting the effect of capillary pressure in the analyses can lead to substantial errors in reservoir parameter determination. Numerical simulation results also demonstrate that the presence of capillary forces accelerates leakoff and consequently the time of closure. For a simulated DFIT-FBA executed in an unconventional gas reservoir, approximately 25% of the total leakoff volume is attributable to capillary forces. Ignoring the effect of capillary pressure results in ~77% overestimation of reservoir permeability. Similarly, for a simulated DFIT case, this contribution is up to 26.5%, leading to ~77% overestimation of reservoir permeability. A sensitivity analysis performed herein underscores the significance of considering interfacial tension and contact angle, while reservoir permeability has a relative influence on the outcomes. Consequently, the early-time leakoff permeability estimated from DFIT/DFIT-FBA tests with current models is more precise for situations where there is reduced interfacial tension between the rock and fracturing fluid, and/or when the rock demonstrates mixed wettability.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"120 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140284984","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 Neural Network for CH4/CO2 Adsorption Characterization","authors":"Hai Wang, Shengnan Chen, Muming Wang, Zhengbin Wu, Gang Hui","doi":"10.2118/218029-ms","DOIUrl":"https://doi.org/10.2118/218029-ms","url":null,"abstract":"\u0000 This study addresses the critical need for accurate characterization of methane (CH4) and carbon dioxide (CO2) adsorption behavior in shale formations, pivotal for optimizing hydrocarbon extraction and advancing carbon neutrality goals. The research introduces a novel approach utilizing Physics-Informed Neural Networks (PINNs) to predict adsorption isotherms across diverse shale cores, integrating Langmuir adsorption theory into a data-driven model. By collecting a limited core dataset and leveraging automatic differentiation techniques, the PINN systematically incorporates physics knowledge into neural networks, compensating for data scarcity and enhancing predictive robustness. The method is validated through statistical analysis, feature selection, and cross-validation, demonstrating its superior performance compared to conventional Machine Learning (ML) models like Random Forest, with a 4.75% improvement in R2 for model performance. Overall, this approach represents a valuable tool for optimizing hydrocarbon recovery, offering insights into competitive adsorption phenomena and paving the way for more efficient and environmentally friendly extraction techniques in complex subsurface environments.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395165","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":"New Insights From an Old Method After History Matching a Newly Designed 1-D Cyclic Steam Stimulation Experiment","authors":"B. Sequera-Dalton, D. Gutiérrez, R. Moore, S. A. Mehta, M. Ursenbach, H. A. García, R. A. Pérez, H. A. Rodríguez, E. J. Manrique","doi":"10.2118/218041-ms","DOIUrl":"https://doi.org/10.2118/218041-ms","url":null,"abstract":"\u0000 A cyclic steam stimulation (CSS) laboratory experiment was conducted with dead heavy oil. Four cycles of steam injection and fluid production were performed, at reservoir pressure, in order to assist in the numerical modelling and understanding of the main mechanisms involved in the process. This was an important part to developing a base model for a broader project evaluating CSS steam-hybrid experiments with live oil. Experimental data, history matching approach and results, as well as key insights are presented.\u0000 An experimental setup, originally designed to evaluate CSS hybrid processes, was improved by fitting a sight glass to identify the fluids flowing out of the opposite core end (into a ballast system), during injection cycles. Dead oil was used to facilitate the analysis of this experiment. Relative permeability curves were tuned to history match each cycle sequentially. Injection periods were matched before production ones in order to estimate the amounts of oil and water displaced to the ballast during injection (unknown although total liquid volumes in the ballast were continuously recorded), which were later injected back into the core during production periods.\u0000 A one-dimensional grid successfully represented the core section while the ballast system was modelled with a production and an injection well. Experimental data such as temperature profiles, pressures and rates were honored. A volumetric ratio of 40% water and 60% oil appeared to be the typical composition of the fluid received by the ballast during injection periods based on simulation results. Fluids reinjected from the ballast back into the core were modelled as an emulsion (i.e., a water-oil mixture). Relative permeability curves were the same for injection and production periods within the same cycle, except for an increased critical water saturation during the last two production periods. One set of relative permeability curves was obtained for each of the four cycles, and are presented in this work. The need to have different curves per each cycle suggests a different flow mechanism was taking place during the CSS test. It appears that the injected steam, after condensing to water, partially emulsified with the heavy oil in the core. Although all the cycles of the CSS experiment were successfully matched using water-oil relative permeability curves, questions about their sufficiency to model heavy oil recovery with steam processes arise. New insights are discussed based on this work and available literature.\u0000 A CSS experiment conducted on a recently commissioned CSS laboratory setup, that mimics the cyclic movement of reservoir fluids with a ballast system, was successfully history matched using a non-traditional approach. The fluids displaced out of the core-into the ballast-during steam injection were re-injected as a water-oil emulsion. New insights from this work underline the need to rethink the traditional way of modelling heavy oil recovery with steam, wher","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"35 S145","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395287","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 Method for Detection of Methane Leaks from Idle and Orphaned Wells Using High-Precision Sensor and a Ventilation Canopy","authors":"Nima Daneshvarjejad, Pooya Khodaparast, Young Cho, Donald Paul, Shahram Farhadi, I. Ershaghi","doi":"10.2118/218022-ms","DOIUrl":"https://doi.org/10.2118/218022-ms","url":null,"abstract":"\u0000 A framework for monitoring and detecting methane emissions from point sources with IOT capability, especially in abandoned wells, is essential to address environmental concerns about reducing greenhouse gases. Our novel design can monitor emissions from abandoned and poorly sealed oil and gas wells in real time, factoring in environmental variables such as weather, moisture, and wind conditions.\u0000 The research presented in this paper is based on data from extensive experimental work using the point source emission of pure methane. To simulate the process, we measured methane emissions using a specially designed canopy enclosing the gas release point with the state-of-the-art sensor that can detect ultra-low leak rates of methane released from a controlled source. The novel canopy system was constructed to mount the sensor and to minimize wind and meteorological effects. Numerous field tests using wide-scale land surfaces were performed to optimize the design parameters of the canopy system. A wind sensor was incorporated into the framework to examine the wind's local, real-time effect on detected concentration and leak rates.\u0000 Using the unique arrangement of our combined canopy, barrier, and local wind sensor system, we can successfully detect and monitor methane emissions from abandoned wells at rates nearing one gram per hour, which is not detectable with previously available technologies. The effect of wind is minimized while allowing for natural ventilation and notifying operators of asset integrity in real-time. This solution decreases costs, and offers scalability, and remote sensing ability.","PeriodicalId":517523,"journal":{"name":"Day 1 Wed, March 13, 2024","volume":"93 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395345","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}