Developing a Smart Workover and Intervention Strategy, Using Automation and Advanced Data Analytics

® Originally appeared in World Oil JUNE 2020 issue, pgs 29-32. Posted with permission. PRODUCTION OPTIMIZATION Developing a smart workover and intervention strategy, using automation and advanced data analytics Selecting wells for workover make more informed decisions faster, re- frame, resulting in missed opportunities. or intervention is time- sulting in economic production gains. Without being able to easily access data consuming, resulting in fewer or automate low-value tasks for screening well reviews per year and INDUSTRY CHALLENGE wells, production engineering teams are longer intervals between the With oil and gas companies operating heavily constrained in implementing well under razor-thin margins, production en- interventions or workovers that deliver pre-planning and execution gineering teams are under increased pres- the maximum economic benefit to their stages. This article presents a sure to squeeze production from their organizations. solution that uses automation existing assets, as efficiently and econom- and advanced data analytics ically as possible. CURRENT STRATEGIES to turn months-long manual A strategic well intervention and During the pre-planning or candidate well selection into a minutes- workover program can help operators screening stage of a traditional well inter- achieve this objective. However, the pre- vention or workover program, produc- long process that supports planning well review process for such tion engineering teams use a multitude of economic production gains. jobs, which involves screening wells, se- applications to collate data, and manual lecting the best intervention or workover engineering techniques to conduct their method, and estimating the post-job well screening and analysis. These analy- ŝ CIO CIO MARIO, RINI SAPUTRA, SERA value of the well against the cost, is often ses are repetitive in nature and limit the SAIFUDDIN and TRIYONO TRIYONO, PT Saka characterized by inefficiency. extent of study when managing high-well- Energi Indonesia; SABRINA METRA, PRAPRUT Time-consuming manual tasks, such count assets. Teams traditionally adopt a SONGCHITRUKSA, RAJEEV RANJAN SINHA as gathering historical and offset well reactive approach to managing the health Figure 1 and KARTHICK VADIVEL, Schlumberger data, and running standard petroleum of their wells. illustrates the stan- engineering techniques across multiple dard methods applied for the candidate wells, prevents production engineers selection stage. from spending more time working on Depending on the scope of the proj- Well workovers and interventions are higher-value engineering workflows for ect, the realization of the workover or essential strategies for operators to sus- planning and execution of a workover or intervention opportunity can take a very tain or increase their production. How- intervention. This considerable time in- long time, sometimes even up to a year. ever, the well review process for these vestment also limits the volume of wells The well screening process alone can take jobs is time-consuming, involving numer- that can be evaluated over a given time- more than three months. ous manual and repetitive tasks to gather data and analyze results, resulting in proj- Fig. 1. Manual analysis approaches for candidate recognition. (Courtesy of Schlumberger) ects taking weeks or even months before getting to the planning and execution stage. In addition, best practices and lessons learned from past interventions are rarely captured, preventing a systematic improvement of the well selection meth- odology and choice of interventions for future well reviews. Leveraging automation and advanced data analytics, Schlumberger, working with PT Saka Energi Indonesia (PGN Saka), developed a well optimization solution that not only automated many of the manual data gathering tasks but also the engineering and economic workflows that are used during the pre-planning stage for a well intervention or workover. This has helped PGN Saka’s engineers World Oil® / JUNE 2020 29 PRODUCTION OPTIMIZATION Due to this lengthy timeframe, reviews the actions can be used to enrich and im- neers, developed a well portfolio optimi- are generally only performed two to four prove the system. zation (WPO) solution termed Saka well times per year, and sometimes less, de- opportunity register, define and selection pending on the operator. This results in CASE STUDY (SWORDS) for the Ujung Pangkah as- significant time lags between the time PGN Saka operates the Ujung Pang- set. SWORDS is a decision-support ad- from review to execution in the field. The kah assets, an offshore oil and gas field in visory system that incorporates decades delay adversely affects the realization of Indonesia. Ujung Pangkah field is under- of proven petroleum engineering analysis production potential. going a major development that involves methods and best practices, combined well intervention, workover and surface with PGN Saka’s business logic, to auto- SOLUTION optimization. To boost overall perfor- matically and autonomously: The adoption of various analytics and mance across the asset, PGN Saka wanted • identify underperforming wells machine learning tools into engineering to uncover opportunities to streamline its through their performance workflows, such as regression and clas- well review process. signatures sification models, make it possible to The main reservoir in Ujung Pangkah • determine root causes automate and improve many of the man- field is Kujung 1 carbonate (K1Z2-3), an • recommend the most appropriate ual processes that are used in a traditional early Miocene platform margin carbonate remedial interventions well workover or intervention programs. reef buildup. It is a multi-layered carbonate • probabilistically quantify post- Using these tools, Schlumberger has reservoir, which produces through multi- workover production developed a knowledge-based system lateral wells. Because it is a mature field, • evaluate risked economic viability (Fig. 2) that provides a standardized, au- the process of selecting wells for produc- of workover candidates tomated approach for the well opportunity tion enhancement projects can be tedious • estimate the chance of success for maturation process (OMP). The system is and challenging. PGN Saka was using a each intervention. based on a hybrid and automated decision- manual process to conduct its well reviews The SWORDS solution runs continu- support system anchored in a knowledge- and relied on each engineer’s individual ously in the background of PGN Saka’s based framework. Hybrid refers to the experience to assess the wells, as most operations, requiring only minimal manu- integration of the petroleum engineering of the best practices and lessons learned al interaction. Thus, PGN Saka’s engineers analysis methods and the operator’s busi- from previous projects weren’t captured. receive constantly updated insights on the ness logic with advanced machine learning In 2018, a workshop was conducted intervention and workover opportunities algorithms, to autonomously identify well to uncover solutions that would increase across the asset. Figure 3 describes the key performance signature and opportunities PGN Saka’s hydrocarbon production and elements of the advisory system. that are best aligned with the operator’s reserves in a cost-effective manner, and The key objectives of the SWORDS economic considerations. accelerate recovery and decision-making solution were to substantially decrease This system can rapidly screen and time. The workshop found that just re- time and effort for well reviews; automate rank high-well-count assets (hundreds viewing the results of the workover can- a manual well OMP, in particular the initial or thousands of wells) in a fraction of didate program took PGN Saka manage- candidate screening; increase decision ac- the time, compared with traditional ap- ment around 200 days. Engineers spent curacy and quality; and enable PGN Saka proaches. Such a solution enables the another 90 to 120 days conducting a field- to go from reactive “firefighting” mode to proactive management of existing wells wide well review to plan annual workover a more proactive approach, and shift engi- by keeping the production enhancement and intervention campaigns. Because of neering focus to higher cognitive engineer- opportunity pipeline full and expediting this significant time investment, PGN ing efforts required for the planning and potential candidates. The system detects, Saka only conducted reviews one to two execution of an intervention or a workover. diagnoses and recommends the appropri- times per year. The SWORDS design introduced new ate actions required to ensure that wells To address this issue, Schlumberger, automation and data analytics technolo- remain healthy. The results captured from working closely with PGN Saka engi- gies and a standard approach to well portfolio optimization for PGN Saka. Working closely with PGN Saka engineers, the fol- Fig. 2. Knowledge-based system for well portfolio optimization. (Courtesy of Schlumberger) lowing components of the new workflow, shown in Fig. 3, were evaluated to see if they could be automated or improved: 1. knowledge base 2. technical screening 3. ranking (production analytics ranking) 4. problem analysis 5. opportunity identification 6. technical potential or gain estimation 7. risk assessment 8. historical success 9. economic analysis. 30 JUNE 2020 / WorldOil.com PRODUCTION OPTIMIZATION Adding a new, robust and integrated An automated decline curve

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