Day 1 Mon, March 13, 2023最新文献

{"title":"Seismic Reprocessing Leads to New Breakthroughs——A Successful Case in the ASH Field, A.G. Basin in Egypt","authors":"Mohamed Elokr, A. Lotfy, Wei Xing, Huijing Li, Ahmed El Bassioni, Nader Moatasem","doi":"10.2118/214066-ms","DOIUrl":"https://doi.org/10.2118/214066-ms","url":null,"abstract":"\u0000 ASH oil field located in the east of the AG Basin in Egypt. Lower Cretaceous Alam El Bueib is the main oil producing formation. Due high heterogeneity of the Abu Roash Members succession in addition to the influence of thick limestone of the Upper Cretaceous and the influence of multiple complex faults, the quality of seismic data is very poor. This is mainly manifested in, a, the variation of vertical velocity in lithology changes resulting a significant error in depth migration; b, the fault imaging is not clear due to low S/N ratio, which leads to serious challenge for structure mapping. Therefore, the seismic re-processing was carried out.\u0000 Two key techniques were carried out to achieve the target, that: uses new well VSP data to adjust the velocity model and CRAM PSDM for re-processing.\u0000 Adjusting the velocity model: The previous PSDM acquired in 2015 had no well control covering the deeper target of Alam El Bueib Formation but the advantage of using VSP data of ASH-3 well as a well control for building new velocity model of the reprocessed data. CRAM PSDM re-processing: The CRAM (Common Reflection Angle Migration) is a cluster-based imaging system that generates conventional reflection angle gathers without azimuth dependency. Optimal local tapered beams are internally created and imaged to form high-quality image gathers, which can then be used in standard interpretation systems for accurate velocity model building and amplitude inversion (AVA).\u0000 Applying CRAM technology in depth migration instead of Kirchhoff depth migration, has a great impact on enhance the final seismic image. The CRAM algorithm is using the Ray path migration of the seismic signal instead of using migration aperture as in Kirchhoff. Applying the ray path migration helps in adopting the seismic trace position ultimately enhance the fault definition and S/N ratio at the deeper target levels.\u0000 As a practical case from the comparable seismic sections, same arbitrary line in different seismic volumes, the reprocessed data showed a high level of improvement in fault definition specially in the north portion of ASH field closer to the major fault which was very poor in the previous data. In the reprocessed CRAM PSDM data, the good amplitude extended towards the north and west portions of the field, which allowed to define the faults through Alam El Bueib horizon and decrease the uncertainty of the new proposed well location in the entire field. (Figure 1)\u0000 Figure 1 Amplitude comparison between reprocessed and original data, ASH field","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"129 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":"132187057","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-Well Distributed Fiber Optic 3D VSP Acquisition Technology for Offshore Exploration in the Middle East","authors":"Qingfeng Li, Jilong Zhao, Yangshuan Fan, D. Huang, G. Yu","doi":"10.2118/214012-ms","DOIUrl":"https://doi.org/10.2118/214012-ms","url":null,"abstract":"\u0000 With the development of distributed fiber optic acoustic sensing technology in the field of borehole seismic surveys, more and more oil fields choose to pre-install fiber optics in the well in advance for borehole seismic data acquisition or fluid detection. In 2022, ADNOC completed the world’s first multi-well DAS VSP and OBN joint acquisition survey in the Persian Gulf, with the fiber placed outside the tubing, relying on the offshore high-density OBN acquisition survey, completing a total of 13 wells for DAS VSP acquisition. The DAS VSP raw data spacing interval is 1 m, the sampling interval is 1 ms, and the offshore air guns are staggered at a density of 25 m × 25 m. This study will focus on the determination of multi-well DAS VSP data acquisition area, data analysis for different Gauge Length, and raw data quality control.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"97 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":"114849030","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":"Joint OBN and 3D DAS VSP Data Acquisition and Processing in Offshore Abu Dhabi","authors":"G. Yu, Haibo Liu, Qingfeng Li, Dong Huang, G. Cambois, J. Cowell, Jane Mason, Yuanzhong Chen, Junjun Wu, Yanbin Zhang, Fei Li","doi":"10.2118/213973-ms","DOIUrl":"https://doi.org/10.2118/213973-ms","url":null,"abstract":"\u0000 The world largest 3D DAS-VSP survey was completed through the joint OBN and 3D DAS-VSP data acquisition project. The data processing workflow includes deblending, noise removing, first break picking, FWI inversion, velocity modeling building and lease RTM processing. The multi-well 3D DAS-VSP data will be used to generate high-accuracy and high-resolution 3D structure image with a higher number of folds, and the attributes of the amplitude preserved imaging data can be used to identify fluid in the reservoir formation and tracing the reservoir layer distribution around the boreholes.The world largest 3D DAS-VSP survey through the joint OBN and 3D DAS-VSP data acquisition project;The data processing workflow includes deblending, noise removing, first break picking, VSP FWI to invert the velocity model and least-squares multiple RTM imaging processing.\u0000 The world largest 3D DAS-VSP survey was completed through the joint OBN and 3D DAS-VSP data acquisition project. By using the airgun sources of an ongoing high-density OBN data acquisition project within the offshore of Abu Dhabi, the high-density OBN data and 3D DAS-VSP data were acquired simultaneously in a total of 13 wells. All the wells have armored optical cable preinstalled outside the production or injection tubing and the optical cable was strapped on the tubing with clampers at 10 m spacing. The data processing workflow includes deblending, noise removing, first break picking, VSP FWI to invert the velocity model and least-squares multiple RTM imaging processing. The multi-well 3D DAS-VSP data will be used to generate high-accuracy and high-resolution 3D structure image with a higher number of folds, and the attributes of the amplitude preserved imaging data can be used to identify fluid in the reservoir formation and tracing the reservoir layer distribution around the boreholes. DAS-VSP data can also been used to enhance borehole-driven 3D seismic data processing significantly. The DAS-VSP data can also be used for AVO analysis, reservoir fracture prediction, and anisotropy analysis and reservoir characterization. At the meantime, the armored optical cable is located near or inside the reservoir, the 3D DAS-VSP data will be used to extract formation average and interval velocity, deconvolution operator, absorption, attenuation (Q value), anisotropy parameters (η, δ, ε) as well as enhanced the OBN seismic data processing including velocity model calibration and modification, static correction, deconvolution, demultiple processing, high frequency restoration, anisotropic migration, and Q-migration.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"31 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":"121904051","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":"Demystifying Dynamic Evolution of Fault System and its Controls on Karsted Reservoirs by Multi-Technology Integration","authors":"Rujun Wang, Yanming Tong, Yintao Zhang, Chuan Wu, Yongfeng Zhu, Gaige Wang, Jiangyong Wu, Pin Yang, Chenqing Tan","doi":"10.2118/213987-ms","DOIUrl":"https://doi.org/10.2118/213987-ms","url":null,"abstract":"\u0000 The Tazhong paleo-uplift is one of the most important hydrocarbon enrichment areas in Tarim Basin. After years of exploration, many reservoirs have been discovered in the deep Paleozoic carbonate karsted rocks. Current research suggests that tectonic evolution and faulting movements have an important impact on reservoir development and hydrocarbon accumulation. But the reservoir prediction reliability is greatly compromised due to the lack of appropriate technical means to ascertain the geometric and kinematic characteristics of fault system more accurately. We solve this difficulty to a great extent by integrating a series of new applicable technologies.\u0000 Firstly, the existing seismic amplitude cube was further processed with the technology of \"vector high fidelity signal enhance\" which enhanced the natural bandwidth of seismic imaged volumes by simultaneously optimizing the signal and frequency content. Then more details of large-scale faults in the target interval were displayed. Secondly, a variety of fault detection methods were used to trace all possible large-scale faults including conventional edge-detection methods such as Variance cube and new method of \"end-to-end convolutional neural network (CNN)\". Thirdly, \"auto fault patch extraction\" was performed and the extracted fault patches were combined with some manual work to make sure they followed certain structural patterns according to the regional geological knowledge. Fourthly, the kinematics of the mapped fault system including its grouping and staging were carefully explored based on \"discontinuity stability analysis\" and traditional geological analysis. The former was based on Mohr-Coulomb criterion considering friction angle and cohesion of the faults, their attitudes and the 3D paleo-stress fields corresponding to different tectonic events. And the latter mainly focused on vertical layering of seismic structures considering vertical variations of fault patterns and strata attitudes, offset formations, stratigraphic unconformities, growth formations and fault patterns in plan view, etc.\u0000 It was suggested that the karsted carbonate reservoirs in some regions were mainly related to 3 types of fault damage zones, i.e. strike-slip fault damage zones, reverse fault damage zones and the hybrid ones. Among them, the parts with larger deformation, pull-apart basin, En echelon structure, faulted anticline, and superimposed elements were the most favorable belts to target high performance producers.\u0000 It is the first time to integrate so many applicable new technologies to ascertain more clearly the geometry and kinematics and the fault system in the deep karsted carbonate reservoirs which are otherwise blurred on seismic images. This can be applied directly for optimized well placement and can also be referred to for similar industrial projects.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"57 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":"125011159","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":"Real-Time Drilling Engineering and Data-Driven Solutions for Upstream Cost Savings: Coupling Earth Models, Digital Twins, Data & Drilling Automation","authors":"Muhammad Rizky Amin, A. Baruno, R. AitAli, Joost De Vreugd, M. Forshaw, M. Mahmoud","doi":"10.2118/214020-ms","DOIUrl":"https://doi.org/10.2118/214020-ms","url":null,"abstract":"\u0000 Despite the market up-cycle efficient use of CAPEX for upstream well construction projects remain a key topic for E&Ps. Therefore, non-productive-time and invisible-lost-time (NPT & ILT) reduction continue to be paramount.\u0000 Wells of such complexity require the introduction of new drilling automation technology to meet the challenge associated with inadequate hole cleaning, vibrations and connections practices resulting of the implementation of the i-Tral real-time monitoring used to mimic manual torque and drag parameters through artificial intelligence and machine learning allowing extrapolation, alarming and therefore early warning onset of friction and stuck-pipe, continuously supply required flowrate and visualize cuttings concentration along the wellbore and real-time multi parameters optimization for drilling dysfunction and penetration rate increasing tool longevity, trips for tool failure hence reduction in section delivery time.\u0000 This paper details the features of the i-Trak torque and drag, Dynamic management and hole cleaning models capable of drive efficiencies to execute effectively and consistently each operations while meeting the goals set in the AFE.\u0000 The technology was first deployed in Abu Dhabi (UAE) in xx Field, a producing conventional oil field located onshore and operated by ADNOC ONSHORE. In total 27083ft was achieved combining Well-I and Well-II where the objectives behind these 2 horizontals wells were to maximize reservoir contact, improve productivity and accelerate delivery while minimize construction costs by implementing mitigations in addressing concerns which helped in streamlining the drilling operations while reducing any potential risks. These wells were drilled in collaboration by ADNOC and Baker Hughes and considered to be the first maximum reservoir contact (MRC) project in this area.\u0000 For primary mitigation, the output of cuttings mass existing in the wellbore (static and mobile) can give a clear indication of the amount of cuttings downhole which corresponds to 50% of the criteria analysis for an inclination range. Secondary mitigation is from the high-resolution torque and drag samples plotted real-time and compared against theoretical pre-well plan broomstick model for different frictions factors measuring the deviations to concisely convey the end users to apply the required procedures to alleviate risks of well constructions operations.\u0000 Finally, the integration of dynamic management provides a unified user interface which combines ROP and VSS measurements in a single place to minimize maintenance and repair costs while driving higher ROP's.","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":"130153197","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":"Solving Processing and Imaging Challenges in a Southern Caspian Sea Complex Structure through Modern OBN Acquisition","authors":"Xiaolin Lyu, Xinlei Hu, Wei Wang, M. Khdhaouria, Lamia Rouis, Aldrin Rondon, G. Ameish, Qunli Qi, Mingqiu Zhao","doi":"10.2118/214081-ms","DOIUrl":"https://doi.org/10.2118/214081-ms","url":null,"abstract":"\u0000 The Greater Cheleken Area (GCA) developed by Dragon Oil, situates on a complex NW-SE dextral transcurrent zone which separates the Northern from the Southern Caspian Basin (SCB). Numerous hydrocarbon accumulations were discovered in highly faulted Pliocene-Pleistocene flower structures of different types. Shallow gas has been identified vastly distributed in this region via numerous site surveys, that creating slow-velocity and high-absorption dimming zone on the seismic imaging. It poses great challenges on seismic processing and imaging.\u0000 In 2022, an OBN seismic campaign was carried out aiming to resolve the imaging problems that exist in a legacy dataset. The main objectives for the 3D OBN seismic survey are: proper imaging of structures, faults and fractures characterization, stratigraphy, petrophysical properties, Gas columns and clouds mitigation and fluid contacts in clastic reservoirs cited between1500-4700m depth. The implementation of the seismic acquisition is successful by delivering high quality data on schedule and within the predetermined budget at the full satisfaction of all involved parties and stakeholders. Strong commitment to HSSE Standards and working as an integrated One-Team with full collaboration and a continuous and close communication between all the Team members are among the main Success Factors.\u0000 A fast track 3D data cube is being produced at the time of writing this manuscript. The preliminary results of the 3D seismic data processing and interpretation are very encouraging and showing a clear improvement compared to the legacy 3D seismic data set.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"13 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":"125586762","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":"Measuring Maturity of Well Integrity Management - Analysis of Well Integrity in Brownfield Using Maturity Models for Prolonged Well Lifecyle","authors":"M. S. Yakoot, A. Salem, O. Mahmoud","doi":"10.2118/214054-ms","DOIUrl":"https://doi.org/10.2118/214054-ms","url":null,"abstract":"\u0000 Well integrity (WI) is a growing concern in the oil and gas (O&G) industry as fields mature and WI problems increase. In project management, maturity denotes having perfect condition to attain the organization's objectives. Applying the same principle to O&G industry, provides a pathway and basis to achieve excellence in WI management in O&G fields. Maturity of WI management has a direct impact on performance, assurance, compliance, and most importantly operation safety.\u0000 A case study from a brown field has been conducted to evaluate the effect of WI management maturity on field performance. The application of well integrity management system (WIMS) in an offshore brown oilfield has been studied. The journey of WI maturity has been presented in detail. All maturity phases have been analyzed starting from program initiation, up to predicting WI failures using machine learning (ML) algorithms.\u0000 Three maturity models, that have been developed by different organizations and individuals, were selected for benchmarking of measured maturity level in the WIMS applied. They were selected as they show independency from industry/organization's type. The three models are; organizational project management maturity model (Opm3), capability maturity model integration (CMMI), and Kerzner project management maturity model (KPMMM).\u0000 The attained results indicated that K-PMMM provides the best description and level determination of maturity level with WIMS applied. Moreover, it is highly recommended to implement maturity models by including all processes and subsystems in the WIMS, to have better resolution of system gaps.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"51 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":"127594545","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":"Real-Time Autoregressive Deep Learning Framework for In-Line Automatic Surface Logging","authors":"A. Alshehri, Klemens Katterbauer, A. Yousef","doi":"10.2118/214079-ms","DOIUrl":"https://doi.org/10.2118/214079-ms","url":null,"abstract":"\u0000 4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas. From AI inspired well logging data interpretation to real-time reservoir monitoring, technologies have led to cost savings, increase in efficiencies and infrastructure centralization. In this work we provide an overview of how autoregressive deep learning methodologies can lead to major advances in the field of formation evaluation and reservoir characterization, providing a comprehensive overview of the technologies developed and utilized in this domain. Furthermore, we provide a future outlook for smart technologies in formation evaluation, and how these sensor-derived data can be integrated. This also describes the challenges ahead. Future developments will experience a growing penetration of 4IR technology for enhancing formation evaluation in subsurface reservoirs.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"194 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":"132088647","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":"Key Success Factors of 3D OBN Survey in a Congested Oil Field in the Caspian Sea","authors":"K. Belaid, Lamia Rouis, M. Khdhaouria, Aldrin Rondon, G. Ameish, Xiaoliang Li, Xiaolin Lyu","doi":"10.2118/213997-ms","DOIUrl":"https://doi.org/10.2118/213997-ms","url":null,"abstract":"\u0000 In this paper, we present and demonstrate that the implementation of an efficient Project Management Strategy has effectively contributed in a safe and successful completion of a very complex 3D OBN Seismic Survey in congested Oil fields. Thus, delivering high quality data on schedule and within the predetermined budget at the full satisfaction of all involved parties and stakeholders.\u0000 Strong commitment to HSSE Standards and working as an integrated One-Team with full collaboration and continuous communication between all the Team members are among the main Success Factors of the 3D seismic survey which was carried out during the critical period of COVID-19. Moreover, the deployment of experienced personnel, advanced and reliable Technologies with adequate equipment have also extended the efficiency of this OBN 3D seismic survey.\u0000 Preliminary results of 3D seismic data processing, interpretation and reservoir characterization are also briefly presented and discussed as a clear enhancement of data quality was already observed compared to the legacy 3D OBC data set.\u0000 A fast track small 3D cube was successfully processed as an utmost and urgent priority for appraisal well selection, design and drilling.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"99 9 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":"128012578","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":"Machine Learning Approach to Predict Pressure Drop of Multi-Phase Flow in Horizontal Pipe and Influence of Fluid Properties","authors":"Ala AL-Dogail, R. Gajbhiye, Mustafa Al-Naser, Abdulkareem Ali Aldoulah, Hulail Yousef AlShammari, Abdullatif Alnajim","doi":"10.2118/214050-ms","DOIUrl":"https://doi.org/10.2118/214050-ms","url":null,"abstract":"\u0000 Multi-phase flow is very common in different applications and industries. In the petroleum industry, multi-phase flow can be observed in different parts of production systems such as tubing of vertical or horizontal wells, flowlines, and surface facilities as well as in the pipeline for exports& transportation of the oil and gas to the refineries. The prediction of the pressure drop is imperative for designing as well as the operation and maintenance of the production system. There are several experimental, theoretical modeling and numerical analyses were carried out to predict the pressure drop of multi-phase flow. The complex interactions of the different phases lead to different flow regimes which are essential for developing the computational model of the pressure drop. Machine learning is a promising approach that can address such complex problems. The objective of this study is to build an Artificial Intelligence (AI) model using dimensionless parameters to estimate the pressure drop of two-phase flow in a horizontal pipe and the influence of fluid properties.\u0000 To achieve the objective of this study, a large set of experimental data was collected which was used to develop the AI model to predict the pressure drop of multi-phase flow in a horizontal pipe. The effect of fluid properties was investigated by changing the liquid properties (density, viscosity, and surface tension). The data was collected by flowing the two-phase air/liquid system on the flow loop with a pipe diameter of 1 inch (2.54 cm) and a length of 30 ft (9.15m). The surface tension was varied using the surfactant solution, viscosity was varied with the aid of glycerin, and density was varied with the aid of calcium bromide. The superficial velocity of the liquid ranges from 0 to 3.048 m/s (0–10 ft/s) and the superficial gas velocity ranges from 0 to 18.288 m/s (0–60 ft/s) respectively. Machine learning was utilized to develop models that can identify the pressure drop of multi-phase flow in a horizontal pipe with the effect of fluid properties.\u0000 Results showed that different AI methods can be used to predict the pressure drop of multi-phase in horizontal pipes with high accuracy with few inputs. The wide range of data was processed by applying a machine learning technique for predicting the pressure drop of multi-phase flow. The models were built using dimensionless parameters to extend their validity for various design and operational conditions. The accuracy was improved by introducing the additional dimensionless parameter for all the models.\u0000 The development in the computational methods emerges a new area of numerical and computational fluid dynamics and presently investigators are exploring the application of AI in resolving complex phenomena such as multi-phase flow. The complex interactions of the different phases lead to different flow patterns, which are essential elements during the development of the computational model of pressure drop.","PeriodicalId":286390,"journal":{"name":"Day 1 Mon, March 13, 2023","volume":"15 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":"133955334","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}