Real-time Surveillance and Analysis Tool for Intelligent Completion - An Ultimate Solution to Oil Recovery and Integrated Operation

Ramizah, A. R.; Gordon, Goh K. F.; Tina, Lee L. T.; Gottumukkala, Varma; Muzahidin, M Salim; Willem, Schalk; Ahmad, Samira; Sanggeetha, K.; Lester, T. M.; Khairul, Akmal M.; Mabel, Chia P. C.; Tamin, Muhammad; Dipak, M.

doi:10.2118/186223-ms

Cited by 4 publications

References 4 publications

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Application of Novel Predictive Analytics for Data Allocation of Commingled Production in Smart Fields

Chia

Yakup

Tamin

et al. 2018

SPE Asia Pacific Oil and Gas Conference and Exhibition

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This paper details out the application of a predictive analysis tool to ‘S’ Field's commingled production, aiming to enhance production allocation and reservoir understanding without the need of well intervention and a reduced frequency of zonal rate tests and data acquisition. Allocation of the production data to its respective reservoirs is performed via a novel Multi-Phase Allocation method (MPA), taking into account the water production trending evolution derived from relative permeability behavior of oil-water in each reservoir to compute flow rates for liquid phases over time. The precision of the derived rates is constrained by actual zonal rates tests through Inflow Control Valves (ICVs). This method will be cross referenced against ‘S’ Field's existing zonal rate calculation algorithm, utilizing input data from well tests results and real time pressure and temperature data. The MPA method demonstrates improvement in the allocation of production data as compared to the conventional KH-methodology as MPA takes into account the water cut trending between reservoirs. Leveraging on ICVs to obtain actual zonal rate measurements, this greatly reduces the range of uncertainty in the allocation process. MPA derived production split ratios closely match the split ratios derived from the ‘S’ Field's existing zonal rate calculation algorithm, which utilizes input data from well tests results and real time pressure and temperature data from down hole gauges. It is observed that the usage of actual measured zonal rate tests reduces the range of uncertainty of the MPA data. A combination of novel multi-phase deliverability models coupled with smart field technologies such as intelligent completions and real-time surveillance and analysis tools will increase the accuracy of the back allocation of multi-phase production data in commingled reservoirs.

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Application of Novel Predictive Analytics for Data Allocation of Commingled Production in Smart Fields

Chia

Yakup

Tamin

et al. 2018

SPE Asia Pacific Oil and Gas Conference and Exhibition

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Intelligent Completions Design and Production Optimization: Application Reality Now Versus the Future In Digital Oilfield

Goh

Biniwale

Musayev

et al. 2020

SPE Annual Technical Conference and Exhibition

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Intelligent Completions (IC) are deployed with the high hopes of frequent data utilization and zonal selectivity maneuver to optimize production continuously. The permanent downhole presence of measurements like pressure, temperature, rate, water-cut, gas-break provide downhole indicators and trending analysis of production performance and injection conformance. These are utilized not only to maximize hydrocarbon production but also to reduce surface handling of water and/or gas, improve injection efficiency, and reduce carbon and environmental footprint. However, the reality could be different from the evaluation stage to the application stage. The asset production engineers or the reservoir engineers face real challenges when it comes to design, downhole installation, data transmission, real-time analysis, and optimization to deliver the real value of the initial investment. These suboptimal application factors, multiplied by the complexity of IC deployment and execution with existing hardware constraints, have limited the progression towards digital well technology. By analyzing such trends, a new advanced completion optimization methodology has been devised, leveraging the latest technology and innovation, IC deployment simplification, and electrification efforts in the industry. This paper analyses the underutilization reasons of digital well technology, such as - the ability of design and implementation, the downhole data measurement, complexity of modeling and optimization, and the bottlenecks in applying the learning from the Intelligent Completions data to optimize production. It is then compared to the easing transition to the future digital-wells, advanced modeling capabilities that are driving the oilfield digitalization by next-generation Intelligent Completion. This digital transition ranges from ease-of-deployment to ease-of-optimization and eventually towards cloud-enabled decision making. The new era of IC electrification deployment and digital solutions are twinning to provide an integrated platform to maximize value and justification for more future digital wells. A fully digital system to control reservoir and optimize the product is becoming a reality with the transformation of modeling capability and enabled by simplification of IC deployment, and this is the digital future of IC optimization. This digital solution is continuously feeding asset subsurface, modeling, and optimization team with productivity or injectivity indexes and other inputs required for reservoir steady-state and transient evaluation. The IC industry continues to be integrating into the new solution frontiers of logging-while-producing, the testing-while-producing capability to the eventual optimizing, modeling-while-producing future, leading towards a true digital oilfield of the future.

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Impact of Fit-for-Purpose Monitoring on EOR Planning

Moreno

Liu

Talabi

et al. 2021

Day 1 Mon, November 15, 2021

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Challenges in the design of efficient EOR field pilots have been discussed and documented in the industry, particularly when it comes to optimization of monitoring plans for technical and economical perspectives. This paper explores the benefits of pilot planning where the monitoring/control strategies are included in the early stages of the design to reduce risk of measurements ambiguity and ensure good quality pilot results evaluation. It addresses the use of new and existing technology in monitoring by highlighting the advantages and challenges of each alternative including potential pairing of complementary options to achieve the pilot objectives including illustration of the use of continuous and sporadic measurements on the evaluation. The proposed approach starts with a review of reservoir performance, heterogeneity and pilot objectives to ascertain the plausible monitoring technologies/strategies to aid during the pilot de-risking, followed by the identification of adequate novel and mature monitoring options, which are specific to EOR type and measurement nature (permanent, time lapse, etc.). Advantages of incorporating the monitoring strategy as integral part of the pilot design, as well as evaluation of the effectiveness/viability in the presence of uncertainty of the selected monitoring alternatives are discussed providing a reference of suitable/plausible EOR specific technologies. The paper illustrates the importance of selecting monitoring alternatives that feed off each other and the importance of using fit-for-purpose evaluation algorithms and a digitally enabled, structured approach to analyze and democratize pilot results and enable actionable decisions in operations.

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Real-time Surveillance and Analysis Tool for Intelligent Completion - An Ultimate Solution to Oil Recovery and Integrated Operation

Cited by 4 publications

References 4 publications

Application of Novel Predictive Analytics for Data Allocation of Commingled Production in Smart Fields

Application of Novel Predictive Analytics for Data Allocation of Commingled Production in Smart Fields

Intelligent Completions Design and Production Optimization: Application Reality Now Versus the Future In Digital Oilfield

Impact of Fit-for-Purpose Monitoring on EOR Planning

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