Reservoir Management Best Practices and Lessons Learned from the Agbami Field

Jeboda, Olatunji; Bodunrin, Abiodun; Atuanya, Chukwuma; Ogidi, Emmanuel

doi:10.2118/193471-ms

Cited by 4 publications

References 4 publications

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Best RM Practices for Managing Drilling Margin: A Deepwater Field Experience

Adegboye¹,

Bodunde²,

Mboto³

2020

Day 1 Tue, August 11, 2020

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In the buildup to the drilling campaign in Agbami, the field encountered disruption in its gas injection capability due to hydrate formation in one of the gas injection manifolds. In addition to this operational challenge was a delay in the arrival of the rig. The impact of these two major issues was higher depletion in reservoir pressures in the field than originally planned, with the attendant narrow drilling margin. This narrow drilling margin posed a significant challenge to the success of the drilling campaign. A multidisciplinary team comprising of Reservoir Engineers, Geologists, Drilling and Completion Engineers collaborated to assess the potential impact of these two issues on forthcoming drilling campaign. The team found out that the shallow reservoirs of the field were most impacted. The team then evaluated multiple options for addressing the foreseeable pressure depletion with its attendant drilling margin challenge. Leveraging the stream of downhole surveillance data from the intelligent completions in the field, reservoir simulation model was used to assess the optimal production and injection rates for the reservoirs, in addition to the priority given to the limited available gas and water for injection into the identified reservoirs that will boost pressure adequately prior to commencement of the drilling campaign. Furthermore, rig schedule optimization was carried out to align the well drill dates to those from the reservoir simulation. This joint effort resulted in improved reservoir pressures in the shallow reservoirs that enabled safe drilling margin during the successful drilling campaign.

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Best RM Practices for Managing Drilling Margin: A Deepwater Field Experience

Adegboye¹,

Bodunde²,

Mboto³

2020

Day 1 Tue, August 11, 2020

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Deep Reinforcement Learning for Generalizable Field Development Optimization

Tang

et al. 2021

SPE Journal

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Summary The optimization of field development plans (FDPs), which includes optimizing well counts, well locations, and the drilling sequence is crucial in reservoir management because it has a strong impact on the economics of the project. Traditional optimization studies are scenario specific, and their solutions do not generalize to new scenarios (e.g., new earth model, new price assumption) that were not seen before. In this paper, we develop an artificial intelligence (AI) using deep reinforcement learning (DRL) to address the generalizable field development optimization problem, in which the AI could provide optimized FDPs in seconds for new scenarios within the range of applicability. In the proposed approach, the problem of field development optimization is formulated as a Markov decision process (MDP) in terms of states, actions, environment, and rewards. The policy function, which is a function that maps the current reservoir state to optimal action at the next step, is represented by a deep convolution neural network (CNN). This policy network is trained using DRL on simulation runs of a large number of different scenarios generated to cover a “range of applicability.” Once trained, the DRL AI can be applied to obtain optimized FDPs for new scenarios at a minimum computational cost. While the proposed methodology is general, in this paper, we applied it to develop a DRL AI that can provide optimized FDPs for greenfield primary depletion problems with vertical wells. This AI is trained on more than 3×106 scenarios with different geological structures, rock and fluid properties, operational constraints, and economic conditions, and thus has a wide range of applicability. After it is trained, the DRL AI yields optimized FDPs for new scenarios within seconds. The solutions from the DRL AI suggest that starting with no reservoir engineering knowledge, the DRL AI has developed the intelligence to place wells at “sweet spots,” maintain proper well spacing and well count, and drill early. In a blind test, it is demonstrated that the solution from the DRL AI outperforms that from the reference agent, which is an optimized pattern drilling strategy almost 100% of the time. The DRL AI is being applied to a real field and preliminary results are promising. Because the DRL AI optimizes a policy rather than a plan for one particular scenario, it can be applied to obtain optimized development plans for different scenarios at a very low computational cost. This is fundamentally different from traditional optimization methods, which not only require thousands of runs for one scenario but also lack the ability to generalize to new scenarios.

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Integration of 4D Monitor to Enhance Reservoir Management: A Case Study of Egina Field

Udoinyang,

Amoyedo,

Omolewa

et al. 2024

SPE Nigeria Annual International Conference and Exhibition

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The Egina field is one of the most recent deepwater assets in Nigeria. The Field was discovered in 2003 and production commenced in December 2018. The production plateau of 203 KBOPD was reached in May 2019. Egina field, which is TotalEnergies’ second operated deepwater asset in Nigeria, located in water depths of about 1500m, is composed of highly faulted turbiditic reservoir units. It lies within the transition zone between the extensional and compressional regime of the Nigerian deep-water basin. This paper presents the integration of time lapse (4D) seismic monitoring to enhance reservoir management of Egina field, with a focus on the application and challenges of the seismic monitoring. Egina 4D M1 Ocean bottom Nodes (OBN) seismic data, which was acquired in October 2021, provided valuable insights into the reservoir dynamics including injected & aquifer water movement, pressure evolution in the vicinity of water injectors and depletion patterns around producers. The 4D seismic data has been used as a static model validation tool, critical input for history match process in several reservoir units, support for well intervention program, optimization of well placement and completion, and the evaluation of infill drilling opportunities. Specifically, we share the successful application of 4D seismic for the identification and screening of well intervention candidates, identification of focus intervals for stimulation (Pseudo-PLT) and the result of the recent 4D guided drilling campaign. Furthermore, we also present the challenges and limitations associated with the applicability of the data.

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Reservoir Management Best Practices and Lessons Learned from the Agbami Field

Cited by 4 publications

References 4 publications

Best RM Practices for Managing Drilling Margin: A Deepwater Field Experience

Best RM Practices for Managing Drilling Margin: A Deepwater Field Experience

Deep Reinforcement Learning for Generalizable Field Development Optimization

Integration of 4D Monitor to Enhance Reservoir Management: A Case Study of Egina Field

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