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

Goh, Kim Fah Gordon; Biniwale, Shripad; Musayev, Rashid; Elfeel, Mohamed Ahmed

doi:10.2118/201283-ms

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…The intelligent fields with ICV allow operational flexibility, monitoring, and interaction during oil production. Besides, it enables the integration of digital reservoir management models while making it more expensive [37], [38]. These wells require more careful evaluation since the potential benefits should be estimated considering uncertainties.…”

Section: Proposed Methodologymentioning

confidence: 99%

Smart Oil Field Management System Using Evolutionary Intelligence

Oliveira¹,

Almeida

Lazo

et al. 2023

IEEE Access

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This research proposes a new flexible intelligent system that manages the inflow control valve to improve oil production. For the efficient management of the smart oil field, the use of optimization algorithms is required. Traditional optimization methods tend to be inefficient in solving such problems due to many variables and the numerous locally optimal solutions, besides the effort of reservoir simulation. Therefore, this work presents the development of a methodology that allows optimizing both the control and the positioning of the valves, maximizing the reservoir Net Present Value obtained through the operation management, and analyzing the deployment cost of intelligent wells and their operational returns. Decisions of inflow control valve placement and its operation, flow control, throughout the reservoir's life cycle are simulated to verify the efficiency of the methodology. In order to evaluate and validate the proposed intelligent system, the methodology was tested by building a new model with three evolutionary algorithms, allowing the placement and control of the flow (valve) as a single problem. The results demonstrated that the proposed approach has significant gains in the increased recovered oil volume and decreased water produced, indicating more efficient and sustainable oil production.INDEX TERMS Intelligent fields, positioning problems, evolutionary computing, control valve flow management, decision support systems.

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Section: Proposed Methodologymentioning

confidence: 99%

Smart Oil Field Management System Using Evolutionary Intelligence

Oliveira¹,

Almeida

Lazo

et al. 2023

IEEE Access

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Real-Time Edge Intelligence and Remotely Controlled Optimization of Intelligent Completion Injectivity

Faizah,

Tan,

Tan

et al. 2024

Day 1 Tue, May 07, 2024

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Intelligent completion injectivity refers to controlling and monitoring fluids injected into reservoirs to enhance oil and gas production at nearby producers. Traditional methods often lack the precision and adaptability required to optimize multi zonal injectivity effectively, particularly in the activation of flow control valves (FCV) to optimize production or injection. In conventional operations, these valves are activated manually on-site by a delayed chain of instruction decided on in town and communicated to an on-site technician. This must be done physically on unmanned platforms, thus adding operational complexity and high operating cost. The intelligent completion system deployed in this work leveraged advanced sensors and actuators to enable real-time adjustments, allowing for a more nuanced and efficient injection process. To enable fully compliant remotely controlled intelligent completions with smart edge computation, the interfaces were managed from design to execution to achieve a fit-for-purpose solution. The system integration test was essential to verify smooth execution with improved injectivity interface panel (i.e., onsite GUI) of zonal rate measurement visualization. Downhole equipment comprised permanent downhole gauges (PDG) and FCV. Surface equipment such as a hydraulic pump system with engineered logics of rate calculation were connected to the downhole flow control valves measurements. Meanwhile surface acquisition units (SAU) were connected to PDG systems. Data from PDG SAU and the hydraulic control system are transmitted from offshore to office via satellite communication or internet. Smart algorithm embedded software was engineered to enable real-time data monitoring and control remotely from offshore. With the automated system, the end users can confidently actuate the FCV on the offshore platform from a device located on offshore or office. The process from receiving information to making a decision to take action, which was previously done in months, can now be done in minutes. This allows water/gas injection optimization by maximizing areal and vertical sweep, enables remote injection control, and provides on-site gas and water single-phase injection rate computation. This means the monitoring rate profiling is now directly provided from site acquisition capability, in contrast to the conventional method of post-processing done in town to first analyze the data and then generate the profile. The intelligent system consists of rate computation algorithms to help the user estimate the water/gas injection rate and back allocation when flow is commingled from multiple zones through FCV. The algorithm uses real-time data that are acquired through the PDGs (i.e., annulus and tubing gauges), with the pressure drop across the FCV, FCV position information, and reservoir properties input into the system. Subsequently, the estimated flow rate is used to calculate the injectivity index, which is applied in optimization to overcome challenges such as uneven zonal split injection and unfavorable zonal water-front propagation due to over- or under injection. Intelligent completions are chosen for wells known to have potential for injection or production optimization from multiple zones. Operational complexity, cost, and resource challenges are the bottlenecks to operations that realize the wells’ full potential. Embedding engineered real-time data monitoring and control has proven to simplify operations, reduce OPEX, and minimize HSE risk. The adoption of these technologies has enabled acceleration in digital transformation program to deliver well and resource productivity, efficiency safety, and performance.

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Advanced Completion Optimization ACO: A Comprehensive Workflow for Flow Control Devices

Elfeel

Goh

Biniwale

2021

Day 1 Tue, March 23, 2021

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There is a growing interest in downhole flow control devices (FCD) as they can be used to counter the effects of reservoir heterogeneity and improve hydrocarbon recovery. The variety of FCD types, sizes, and specifications makes it challenging to select the right device, providing an optimal investment return. Reservoir engineers play an essential role in identifying and evaluating the possible options based on numerical simulation models. This paper utilizes a transient numerical optimizer, designed for FCD with active controls, as a generic tool for lower completion optimization, including passive and autonomous FCD. The workflow consists of five steps. The first step is to determine a practical number of completion zones in a well, given the reservoir heterogeneity and completion string considerations. The second step is to explore the potential gains of downhole control without accounting for device-specific limitations. Here, we integrate a local optimization method to run several simulations with reactive and proactive strategies. In the third step, we contrast the local optimization simulations results to determine the suitable family of FCD: passive, autonomous or active. In step four, we ensure the selected FCD family is correctly modelled in reservoir simulation, particularly for autonomous devices, based on a recently published method to calculate equipment specific flow coefficients. Finally, optimization runs are performed using the selected FCD family and specific coefficients. The workflow is demonstrated on a synthetic carbonate sector model with a ~2,000 m horizontal well. It can be shown that analyses of the local optimization results provide quick guidelines to screen and select suitable lower completion equipment for the well and reservoir. Furthermore, the local optimization results can be used to design the selected lower completion string. The suggested workflow is the first of its kind in that it caters to all FCD families. The workflow uses an efficient local optimization method in a next-generation reservoir simulator. The efficiency gains from using the optimizer allows the decision-making time required for FCD evaluation to be an order of magnitude less than the logging and completion operation timeframe. Hence, the workflow enables efficient real-time design, planning and optimization of FCD.

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

Cited by 3 publications

References 26 publications

Smart Oil Field Management System Using Evolutionary Intelligence

Smart Oil Field Management System Using Evolutionary Intelligence

Real-Time Edge Intelligence and Remotely Controlled Optimization of Intelligent Completion Injectivity

Advanced Completion Optimization ACO: A Comprehensive Workflow for Flow Control Devices

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