Well Intervention Opportunity Management Using Artificial Intelligence and Machine Learning

Shekhawat, Dushyant; Devgun, Vishal; Bhatt, Bhartendu; Sinha, Rajeev Ranjan; Songchitruksa, Praprut; Vadivel, Karthick; Dhiman, Adarsh; Jain, Apurv; Shaha, Parimal Unmesh; Holy, Ralf

doi:10.2118/211824-ms

Cited by 7 publications

References 19 publications

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The Intervention Opportunity, Why the Industry does not do More and How New Collaborative Workflows with Aligned Outcomes can Change this

Billingham¹,

Proud²,

Ramondenc³

2023

SPE/ICoTA Well Intervention Conference and Exhibition

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This analysis challenges the typical way interventions have been planned and executed, both from an operational and commercial basis, and examines where there is room for significant improvement in the industry. Perhaps more importantly, it examines the case for performing interventions and tries to explain the headwinds in what is an opportunity for both financial and net zero goal reasons. Benchmarked data has already shown that opportunity absolutely exists to do more, and the authors discuss why the intervention opportunity is underserved. By appreciating the issues operators face when justifying and designing intervention activity, the challenges can thus be addressed by proper alignment to the best outcome. Intervention global expenditure is a small percentage of the total exploration and production spend while there is a strong value case for such operations. This study examines why this is so and then looks at how to address those issues. There is a huge array of well integrity and reservoir performance challenges that can bottleneck production and the industry has delivered many innovative solutions to address these issues. Reduced capital expenditure spend over the last years and the pressure to maintain production sustainably should create a perfect climate for intervention. However, an asset mindset that is often risk averse to entering a producing well, as well as complex workflows, will too often detract from the opportunity to intervene. New workflows—including digital—are discussed to demonstrate how identification of candidate wells and intervention techniques can be simplified, and how the success rate of the operations, as well as incremental production gains, can be determined more reliably to enable more robust outcomes. However, current contracting techniques and conventional key performance indicators can also cause further misalignment as to the true goal of interventions being to increase production sustainably. Those issues and how they have been resolved are addressed herein. New workflows and commercial models have been used to facilitate the quicker identification of intervention opportunities, enabling collaborative planning and optimal solution identification, combined with feedback mechanisms to ensure continuous close collaboration between technical experts enabled by digital tools can disrupt the conventional intervention model. Case examples will be provided to support the arguments made and demonstrate a new way of performing interventions. New digital workflows combined with strong collaborative, technical domain knowledge and a wide array of possible intervention solutions can change current typical intervention models. With these changes further improvements can then be made to the conventional business models used to maximize the intervention opportunity and the sustainability opportunities it brings with regard to getting the most out of existing infrastructure.

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The Intervention Opportunity, Why the Industry does not do More and How New Collaborative Workflows with Aligned Outcomes can Change this

Billingham¹,

Proud²,

Ramondenc³

2023

SPE/ICoTA Well Intervention Conference and Exhibition

View full text Add to dashboard Cite

show abstract

The Intervention Opportunity: Why the Industry Does Not Do More and How New Collaborative Workflows with Aligned Outcomes Can Change This

Billingham,

Proud,

Ramondenc

2024

SPE Journal

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Summary This analysis challenges the typical way interventions have been planned and executed, both on an operational and a commercial basis, and examines where there is room for significant improvement in the industry. Perhaps more importantly, it examines the case for performing interventions and tries to explain the challenges (“headwinds”) in what is an opportunity to achieve both financial and net-zero emissions goals. Benchmarked data have already shown that opportunity absolutely exists to do more, and we investigate why the intervention opportunity is underserved. By appreciating the issues operators face when justifying and designing intervention activities, the challenges can thus be addressed by proper alignment to the best outcome. Intervention global expenditure is a small percentage of the total cost of exploration and production, and yet there is a strong value case for such operations. This study examines why this is so and then looks at how to address those issues. There is a huge array of well integrity and reservoir performance challenges that can bottleneck production, and the industry has delivered many innovative solutions to address these issues. Reduced capital expenditure over the past years and the pressure to maintain production sustainably should create a perfect climate for intervention. However, an asset mindset that is often risk averse to entering a producing well, as well as complex workflows, will too often detract from the opportunity to intervene. New workflows—including digital—can simplify the identification of candidate wells, and intervention techniques can help determine the success rate of the operations, as well as incremental production gains, more reliably to enable more robust outcomes. However, current contracting techniques and conventional key performance indicators can also cause further misalignment as to the true goal of interventions being to increase production sustainably. Those issues and how they have been resolved are addressed in this study. New workflows and commercial models have been used to facilitate the quicker identification of intervention opportunities, enabling collaborative planning and optimal solution identification, combined with feedback mechanisms to ensure continuous close collaboration between technical experts enabled by digital tools, which can disrupt the conventional intervention model. Case examples support the arguments made and demonstrate a new way of performing interventions. New digital workflows combined with strong collaborative, technical domain knowledge and a wide array of possible intervention solutions can change current typical intervention models. With these changes, further improvements can then be made to the conventional business models used to maximize the intervention opportunity and the sustainability opportunities it brings with regard to getting the most out of existing infrastructure.

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Automated Production Enhancement Candidates Screening Powered by Machine Learning Unlocks Untapped Potential in Matured Oil Fields – A Case Study

Mat Khair,

Zaizakrani,

Zul Azhar

et al. 2023

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Field A, located offshore East Malaysia, has been a productive conventional oil field for more than three decades. Being a mature or brown oil field, it encounters several surface and subsurface including sand production, increasing water-cut and reservoir pressure depletion. Consequently, field production declines over the years. These issues have compelled the team to pursue for options to optimize the existing Field A's producing and idle wells in an effort to slow the production decline rates and keep up with field deliverability. Failure to recognize and manage well behaviors may cause producers unable to meet production targets and experience other serious problems, such as well integrity. Hence, Production Enhancement Candidate Generation and Screening (PECGS), a Machine Learning (ML) tool, have been utilized in Field A to identify potential candidates for Production Enhancement (PE) from both active and inactive wells. The system has been able to establish a workflow that is capable of guiding and navigating the engineer through the process of identifying candidates for PE, such as zone changes (ZOC), Behind Casing Opportunities (BCO), add perforations and well stimulations that can be tracked all the way up to the execution stage. This paper presents the processes of identifying production enhancement opportunities, as well as the methodology utilizes to identify underperforming candidates and analyze well integrity issues. It describes how data science and engineering analyses are integrated, and stresses how collective insights are gained from quantitative and qualitative outcomes, such as production increases, cost savings, operational efficiencies, and overall impact on field performance. The implementation of well-engineered workflows has significantly aided and enabled users in identifying low-hanging fruit opportunities. This solution has created an efficient working environment, particularly for the team in Field A, empowering them to identify and capitalize on PE and Idle Well Reactivation (IWR) potential opportunities. By emphasizing the overall process flow from identification to results, this paper aims to provide a comprehensive understanding of the PE journey in Field A, showcasing the effectiveness of the approach and its contribution to the field's performance improvement.

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Well Intervention Opportunity Management Using Artificial Intelligence and Machine Learning

Cited by 7 publications

References 19 publications

The Intervention Opportunity, Why the Industry does not do More and How New Collaborative Workflows with Aligned Outcomes can Change this

The Intervention Opportunity, Why the Industry does not do More and How New Collaborative Workflows with Aligned Outcomes can Change this

The Intervention Opportunity: Why the Industry Does Not Do More and How New Collaborative Workflows with Aligned Outcomes Can Change This

Automated Production Enhancement Candidates Screening Powered by Machine Learning Unlocks Untapped Potential in Matured Oil Fields – A Case Study

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