Day 1 Mon, March 13, 2023最新文献

{"title":"Optimizing a Complex, Uneven and Low-Resolution Reservoir While Using Dynamic Dataset for Structure Definition","authors":"Sheraz Ahmed, G. Waqas, Hafiz Mustafa Ud Din Sheikh, Saad bin Abrar, Majid Siddiqui","doi":"10.2118/214000-ms","DOIUrl":"https://doi.org/10.2118/214000-ms","url":null,"abstract":"\u0000 It is well established that the oil & gas industry has long surpassed its plateau for large discoveries. Thus, many companies have shifted their focus to cost-constrained policies for hydrocarbon discovery, making it difficult to have a strong sub-surface definition. Add the structural uncertainties and complexities of new discoveries in this scenario, the optimized field development becomes a real challenge. This study presents the development strategy established for a reservoir, with low seismic resolution, using the integrated dynamic data to define the reservoir structure and optimize its recovery.\u0000 This paper focuses on a relatively new discovered formation in one of the oldest gas giants in Pakistan. The productive sandstone units are in beds with thicknesses ranging from 10-50m, separated by mudstone intervals. The low seismic resolution has posed a major challenge in finding the sweet spots for hydrocarbons above Gas-Water-Contact (GWC)-resulting in 60% well failures. Therefore, a workflow was developed to analyze various dynamic datasets in conjunction with the re-interpretation of seismic to delineate the reservoir structure. This included re-interpretation of MDT data and formation gradients, core re-evaluation, critical analysis of reservoir pressure variation, well failure analysis, thickness maps and PVT properties.\u0000 The first four wells drilled in this reservoir had two successes, both in the Western compartment. However, sudden water production loaded up these wells only after 123 BCF production, which was a lower recovery as compared to GIIP estimates. After further geological evaluation and 3D seismic re-acquisition, more wells were drilled – revealing another deposition with a different GWC, but only 1 well was successfully completed as a producer, adding around 38% more reserve, while the others were again unsuccessful due to high structural uncertainty. This led to the development of a detailed algorithm for integrating dynamic data set with seismic re-interpretation and thickness mapping with the help of which two more wells were drilled and added around 22 % more reserves to the current mix. The dynamic data of these wells have now been further evaluated which revealed that the two compartments are in fact in communication with each other despite having a 60m difference in their GWCs. Finally, two more wells are now planned which will add around 10-20 % more recoverable volumes, giving an overall EUR of ~80% from these compartments.\u0000 The main achievement of this workflow is a robust algorithm to integrate the dynamic data with geological interpretation to delineate a low-resolution reservoir since the seismic interpretation cannot be solely relied upon for developing such reservoirs. The paper also illustrates the robust engineering models and data analyses in a more systematic manner to ensure optimum locations for future wells to access, the otherwise, undrained locations.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124803770","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 Velocity Modeling by Water Table Imaging and Structural Smoothing in Yingxiongling, Qaidam Basin","authors":"Chenqing Tan, Yongsheng Wang, Yanming Tong, Rongsheng Ou, Chuan Wu, Yufei Yan, Changlei Zhao, Xiang Li, J. Cui, Yongcang Dong, Rong Li","doi":"10.2118/213990-ms","DOIUrl":"https://doi.org/10.2118/213990-ms","url":null,"abstract":"\u0000 The Yingxiongling structural belt is located in the south of world's highest-altitude petroliferous basin, Qaidam Basin. It is well known for the extremely complicated topography and subsurface structure. Accurate velocity modeling and depth imaging is extremely difficult, as well as exploration and production in this area. Building a reliable initial model that accounts for both near-surface and subsurface structures is crucial. We first generate a geologically-guided subsurface velocity model by structural smoothing of the pre-stack time migration (PSTM) velocity guided by dip field. Furthermore, we take the water table, a typical geological phenomenon in Qaidam basin, into account for near surface modeling. With information regarding a stable and smooth water table, we achieve an optimum image predicting the trap, thus supporting exploration and production in the area.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114928775","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":"Lower Completion Design for Water Control Inflow Based on Logging While Tripping Data, A Case of Study","authors":"R. Celma, Hela Douik, M. Almarzooqi, Abdulaziz Alawadhi","doi":"10.2118/214067-ms","DOIUrl":"https://doi.org/10.2118/214067-ms","url":null,"abstract":"\u0000 Oil production nowadays become more challenging worldwide from high depletion and water production. New devices for water control are available in the market, but an integration with borehole data is critical for optimizing the ratio between oil recovery and water constraint. In this case study, the production forecast for the well was of 600 bd with no water, however the initial well test gave a production of 50 bd with 95% water cut. The main reason behind this contrasting result was unexpected structural features connected with lower aquifers.\u0000 A lower completion design with water control devices was put in place based on open hole data. To reduce the uncertainty and optimize the design, a logging acquisition of resistivity and caliper was suggested to evaluate the remaining fluids of the reservoir and how much of the water was related to reservoir depletion. Due to a rig restriction (a hoist unit) a pipe convey method to acquire data through logging was not possible. Instead logging while tripping was utilized, and we could successfully acquire a reliable caliper, resistivity, and density neutron readings. These results helped us refining the design, optimizing the amount of control devices, and securing the packer placement in the best zones for proper swelling.\u0000 After the flow to clean process, the well tested 250 barrels of oil with 20% water cut which is aligned with the prediction during the design and planning stage.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127832583","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":"Artificial Intelligence (AI) Based - Under Suspended Load Detection - Case Study in Rokan Drilling & Completion Operation","authors":"F. F. Rizki, A. Santoso, Ari Sukma Negara, Bayu Raka Janasri, Bima Surya Khoirul Fikri","doi":"10.2118/214018-ms","DOIUrl":"https://doi.org/10.2118/214018-ms","url":null,"abstract":"\u0000 Automatic under suspended load detection system is a term for an image processing method which is used to identify compliance and safety aspect of crane or lifting & rigging operations in drilling & completion activities especially under suspended load for line of fire prevention which possibly leads to a serious incident and fatality. According to company data statistics, approximately 30% of safety findings are contributed by lifting & rigging operations. As a state-owned company that operates one of the largest fields in Indonesia with an extensive drilling and well intervention programs, Pertamina Hulu Rokan (PHR) commits to protect the safety of their people by reducing lifting & rigging operations safety findings and improving its monitoring process. The company has taken the initiative to explore any digital alternatives that can be applied such as utilization of computer vision and artificial intelligence in online Closed-Circuit Television (CCTV) units to enable prevention of under suspended load case in drilling & completion operations by using deep learning algorithm such as Yolov4 and/or Faster R-CNN.\u0000 During development process, the team has several technical challenges to be addressed such as the diversity of lifting and rigging scenarios such as lifting direction, lifting equipment type, and load variety to capture and detect under suspended load zone in 2-dimensional image using straightforward logic. The first step towards building this system is collecting lifting and rigging operations image datasets from various rig areas and with different lifting scenarios and equipment. Deep learning algorithm such as Yolov4, Faster R-CNN are being used to train the model using the dataset which has been labelled on specific objects related to the lifting operation such as crane boom, crane hook, crane loads, crane tires, crane cabin, and crane box to construct under suspended load zone on the given scenarios. The Preliminary results indicate that the method has been useful to identify under suspended load zone and deliver real time automatic notification during lifting and rigging on a drilling or well intervention operations and prevent the safety risk exposure of our personnel.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125504967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Deep Learning in First-Break Picking of Shallow OBN Data","authors":"Wei Wang, Jiangtao Liu, Xiaolin Lyu, Xin Hu, Yifan Li, Lamia Rouis, M. Khdhaouria, Aldrin Rondon","doi":"10.2118/213983-ms","DOIUrl":"https://doi.org/10.2118/213983-ms","url":null,"abstract":"\u0000 \u0000 \u0000 As well known, the modeling of the near-surface from first-break plays a significant role on the sub-surface imaging, reservoir characterization, and monitoring. Small errors in first-break picking can greatly impact the seismic velocity model building, so it is necessary to pick high-quality travel times. Geoscientists from around the world continues trying their best to address the near-surface challenges. Due to the rapid development of high-efficiency acquisition technique, such as WBH (wide-azimuth, broadband and high-density) acquisition technique and blended source acquisition technique, the quantity of seismic data, especially 3D seismic exploration, has leapt from GB to TB(some to PB), which sets a big challenge for first-break picking. Traditional first-break picking methods can't meet the production. In recent years, with the development of computer capacity and algorithm, artificial intelligence has changed our lives in many ways. In seismic exploration, artificial intelligence, like deep learning, has played a more and more important role now, from fault prediction, attribute identification to velocity and first break picking. Generally, deep learning is a new neural network which has multiple hidden layers, mostly over 3 layers, compared with traditional neural network. Deep learning includes Deep Belief Network (DBN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and so on. In this paper, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been combined for first-break picking in a large 3D OBN project of Caspian Sea. A high precision near seabed velocity model is built based on the auto-picked first break with tomography inversion, which provides a good solution for static problem of the survey.\u0000","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129221781","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 Field Development Plan for Reliable Production Forecast Using Data Analytics and Artificial Intelligence","authors":"Yanfidra Djanuar, Qingfeng Huang, J. Thatcher, M. Eldred","doi":"10.2118/214021-ms","DOIUrl":"https://doi.org/10.2118/214021-ms","url":null,"abstract":"\u0000 Having a robust field development plan (FDP) for mid-size mature oil fields generally poses considerable challenges in the context of the integrational elements of production forecast, operational environment, projects and surface facilities. An integrated FDP combined with data analytics and artificial intelligence (AI) has been introduced and deployed in a heavily compartmentalized offshore field of Turkmenistan.\u0000 An integrated approach through data-centric analytics and AI has been proposed for an optimal FDP. It consists of four aspects: model integration, time-series forecast (TSF) of production, AI-assisted operation-schedule generation, and evaluation and selection of scenarios. Firstly, model integration is performed as bringing together both multi-discipline raw data from field measurement and their interpretations that change non-linearly. Secondly, model integration aids in the application of AI for production forecast. A unique AI technique was built to allow raw data and interpretation. Illustratively, the model is capable of forecasting decline curves matching the history production. Meanwhile, engineers’ production forecast inheriting from simulation, machine learning or type curves is also constructed by understanding how/why human-driven forecasts differ from the measured decline and incorporating those insights. In addition, AI-assisted scheduler efficiently allocates resources for operational activities, considering the well planning nature, intrinsic operation properties, project planning process, surface facilities and expenditures. Resources are thus utilized for optimal schedules. Finally, evaluation and selection of FDP scenarios take place by considering the multidimensional matrix of factors. Multiple scenarios are generated and scored, reacting to the change of factors. AI-powered optimization is availed to recommend the most efficient tradeoffs between production and carbon generation.\u0000 The implementation of the integrated FDP approach has been successfully applied for the generation of production profiles and operation schedules, which reduces the time by 80% and increasing accuracy by 55%. Production forecast for existing wells and future wells proved to be reliable. It achieved the production targets with proper allocation of schedules, by considering multi-discipline constraints. Through AI-assisted scheduler, different types of rigs were properly assigned to the planned wells, which requires additional rigs based on the outcome. The model was agile to the change and sensitivities of wells requirement, projects uncertainties and cost changes. The optimum FDP scenario was recommended for the business decision, operation guide and execution.\u0000 This approach represents a novel and innovative means of integrating and optimizing FDP considering complex factors using AI methods. It is efficient in merging raw data and interpretations for model integration. It accommodates changes and uncertainties from multiple aspects ","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130169393","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":"Research on Pore Pressure Prediction Technology of HTHP Wells in South China Sea Based on Machine Learning","authors":"Dongsheng Xu, Jin Yang, Yuhang Zhao, Jianchun Fan, Yanjun Li, Xun Liu, Kejin Chen, Zehua Song, Xun Zhang, Hong Zhu","doi":"10.2118/214059-ms","DOIUrl":"https://doi.org/10.2118/214059-ms","url":null,"abstract":"\u0000 The Yingqiong Basin in the South China Sea is located at the intersection of the Eurasian and Indo-Chinese plates, with complex geology and often accompanied by abnormally high pressure. In this paper, we analyze the causes of anomalous high pressure in the South China Sea and analyze the commonly used machine learning methods, support vector machine and BP neural network, and use both methods to predict a block in Yingqiong Basin. The field application was carried out using this method, and the application showed that the prediction accuracy exceeded 95%, the complexity was reduced by 42%, and the drilling efficiency was improved by more than 53%, which played a good guiding effect to the field.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121348730","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":"Corrosion Management Framework: A structured Approach to Managing Corrosion in Oil and Gas Facilities","authors":"Paschal Odinde, Humphrey Ezeifedi, J. Akanni, Nnaemeka Anyachor, Alonge Oladipo, Chibueze Ohia, Chinenye Orajiaka, Habib Sangoyinka, Chris Onuorah","doi":"10.2118/214037-ms","DOIUrl":"https://doi.org/10.2118/214037-ms","url":null,"abstract":"\u0000 The rising cost of corrosion is alarming especially in ageing oil and gas production facilities. This situation is necessitated majorly by absence of corrosion management framework (CMF) which is a blueprint for identification and mitigation of common threats associated with corrosion in production assets. This paper explores the gains, challenges and improvement areas arising from development and implementation of robust corrosion management framework in fifty (50) oil and gas facilities.\u0000 The CMF was developed and implemented using threat identification, barrier design and barrier maintenance philosophy. The result of the CMF implementation revealed that most of these facilities are laced with unmitigated corrosion threats which predisposes the assets to hydrocarbon leaks. The results also provide insight as per the status of the pressure equipment, information for development of risk-based inspection plan and continuous improvement of corrosion management system. Some of the challenges encountered in effective CMF implementation for these facilities includes incompleteness of inspection data, unavailability of design data, absence of leadership commitment, competence gap and cost. This study has shown that CMF implementation is very critical in demonstrating sustainable asset integrity management and return on investment (ROI). However, to deepen the gains of CMF implementation in asset integrity management, there is need to have a well-defined roles and responsibilities, clear leadership commitment, visible corrosion management policy, digitalization of corrosion and inspection data acquisition, as well as competence development.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127514625","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":"Multi-Surveys Merge by Dynamic Warping","authors":"Chenqing Tan, Yongsheng Wang, Chuan Wu, Xiang Li, Yanming Tong, Yongcang Dong, Heng Shi, Quanbing Niu, Jie Cui, Xiangfeng Dai, Pi Xiong","doi":"10.2118/214039-ms","DOIUrl":"https://doi.org/10.2118/214039-ms","url":null,"abstract":"\u0000 The Yingxiongling structural belt is located in the south of the world's highest-altitude petroliferous basin, Qaidam Basin. The complete understanding and interpretation for the entire survey is becoming significantly more important for exploration well deployment. Survey merging has become urgent given the rising demand for further exploration and production in the area. In this paper, we have merged five blocks in the northwest into one. Dynamic warping plays a key role for post-migration data merge. With proper application of this technology and sophisticated model editing, we can minimize the time differences between different blocks. Finally, we provided a good subsurface seismic image for the further exploration evaluation of the Yingxiongling structural belt.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121956606","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":"Innovative Performance Models for Integrated Projects in Offshore Deepwater Operations","authors":"Andres Felipe Nunez Davila","doi":"10.2118/214064-ms","DOIUrl":"https://doi.org/10.2118/214064-ms","url":null,"abstract":"\u0000 Integration in offshore drilling environments was traditionally limited to bundled services and managed by a project manager. The success of the integration model was mainly related to equipment logistics and quality assurance. The objective of this paper is to describe how the traditional model has evolved from a bundled long-term service commercial relationship to the innovative performance award model that has been implemented in an integrated project in the Gulf of Mexico.\u0000 The offshore and deepwater drilling environment is characterized by its high operating cost; thus, risk management is one of the key aspects for the project's success. The oil and gas industry has continuously evolved during the last decades, moving toward digital and automation technologies and applications. The new performance model is based on a time definition and further estimation of budget required. Factors are linked to specific rewards in case the predefined objectives are achieved. The associated risks definition becomes critical to ensure the predefined estimates are achievable. To support the risk management process, the application of digital solutions support the planning and execution stages.\u0000 The multiwell performance analysis and benchmarking tool enables us to evaluate the historical results and comparison between rigs, including and not limited to determining the project baseline, identifying the historical invisible lost time, and defining the main areas of improvement.\u0000 This tool supports the definition of the risks factors and provides the inputs to determine the most accurate project budget.\u0000 For the execution, one of the historical associated risks was related to the drilling fluids and well integrity. It is commonly found in the area for wells to be drilled under narrow operating windows and having extended sections. The increase of frequency and accuracy of drilling fluid properties verification is fundamental to ensure the system is under the required parameters and properties limits previously determined. The automated rheometer has been developed to increase the frequency and accuracy of rheological drilling fluid properties, thus minimizing the impact of one of the most relevant risks across the area.\u0000 Finally, the focus to optimize the schedule and logistics was based on synergies developed across the different services involved. This included activities that are carried out from a single point of contact, eliminating the duplication in tasks and minimizing the resources required.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129092677","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}