Utilizing Lean & Machine Learning to Monitor and Managing Production Induced Subsidence in a Mature Oil and Gas Field and to Ensure Safety of 200+ Field Staff and Safeguard More Than 200 Mln BOE Natih & Shuaiba

Title

Utilizing Lean & Machine Learning to Monitor and Managing Production Induced Subsidence in a Mature Oil and Gas Field and to Ensure Safety of 200+ Field Staff and Safeguard More Than 200 Mln BOE Natih & Shuaiba

Subject

Machine learning
Gasoline
Oil fields
Gas industry
Petroleum prospecting
Seismology
Contractors
Data integration
Compaction
Information filtering

Description

The MicroSeismic (MS) events, also referred to as tremors or induced seismicity, can be triggered by reservoir depletion and compaction as a result of hydrocarbon production with time. In order to measure and locate the MS events in the Field A
Petroleum Development of Oman (PDO) installed many downhole geophones and accelerometers across the field since 2011. The monitoring network allows subsurface teams to understand magnitude, location and depth of the events. Till the end of 2019 a total of 5,597 MS events were recorded and analysed. In 2020 a new Standard Operating Procedure (SOP) was established moving away from a partly manual data information system to an automated real-time data system named PetroAlert (this is ESG invention). The SOP also defines a clear step-by-step action plan and line-of-sight using a color code system (Traffic Light System). The key challenges that needed to be overcome were: 1) problem breakdown, goals and root causes and 2) data integration and IT infrastructure. The first challenge was overcome by utilizing Lean and organizing a KAIZEN event to ensure objectives were clear to all involved team members. The second challenge was solved in consultation with our external event processing contractor the Engineering Seismology Group (ESG) and the PDO geophysics teams (Exploration Directorate). PDO behaviors: - Speed: the new automated alert system is much Leaner and efficient compared to the previous manual system saving 100s of man hours per year. The line-of-sight captured in the SOP makes it clear for the team how to respond and who to inform in case of significant MicroSeismic events. - Leadership: the Gas Team has lead the change with other compaction team members. In principle all information was available but needed to be combined into a simple alert system with appropriate data filtering. - Team work: without teamwork inside PDO with the Lean team, the specialist geophysicist and our external contractor ESG we would not have succeeded. Several Lean sessions (KAIZEN, Gemba, and huddles) were organised to ensure all team members were well informed on the progress and deadlines for the project. The digital transformation in MicroSeismic monitoring in Field A protects 200+ staff in the field and multiple hundreds BOE production in both Natih and Shuaiba Reservoirs. This work can be replicated for other fields in PDO impacted by compaction (Field B and Field C) to increase the success even further. Also it can be replicated worldwide. Copyright 2021, Society of Petroleum Engineers

Publisher

2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021, November 15, 2021 - November 18, 2021

Date

2021

Contributor

Al Wahaybi, Maryam Humaid
van Gilst, Roeland
Salmi, Fathiya Hilal
Wadhahi, Taimur Al
Azri, Saif
Mahruqi, Abir
Siyabi, Qais Ali
Mahajan, Sandeep
Mahrouqi, Khalid Abdullah
AL Siyabi, Nabil Salim

Type

conferencePaper

Identifier

10.2118/208147-MS

Collection

Citation

“Utilizing Lean & Machine Learning to Monitor and Managing Production Induced Subsidence in a Mature Oil and Gas Field and to Ensure Safety of 200+ Field Staff and Safeguard More Than 200 Mln BOE Natih & Shuaiba,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/24023.

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