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All Journal Scientific Contributions Oil and Gas
Hariyadi Hariyadi
UPN Veteran Yogyakarta
Chemical Engineering, Chemistry & Bioengineering Energy

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The Application of Machine Learning (DT-Chan-Performance) in Determining Idle Well Reactivation Candidates at PT. Pertamina EP Regional 4 Zone 11 Cepu Field Sayoga Heru Prayitno; Boni Swadesi; Hariyadi Hariyadi; Damar Nandi Wardhana; Herlina Jayadianti; Geovanny Branchiny Imasuly; Indah Widiyaningsih; Ndaru Cahyaningtyas
Scientific Contributions Oil and Gas Vol 48 No 2 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i2.1657

Abstract

Indonesia faces a significant challenge in achieving its goal of oil production 1 million barrels of oil per day by 2030, particularly as it relies on old fields or mature fields (brownfields) to extract remaining hydrocarbons. One of the strategies involves reactivating of idle wells in Cepu field, managed by PT. Pertamina EP Regional 4 zone 11. This study focuses on identifying suitable candidates for reactivation through combination of research, innovation and production-focus analysis. The process begins with problem definition, aiming to understand the factors influencing idle wells and review recent advancements in reactivation prediction. Data were collected from both primary and secondary sources covering period 2018-2023. The next stage is implementing Machine Learning (ML), specifically Decision Tree (DT) model, to overcome problems related to data accuracy and complexity. A web application was developed to support decision-makers in selecting wells with high reactivation potential which can provide the best solution of increasing oil recovery. The research results show a high success rate on Accuracy Under Curve and Receiver Operating Curve score of 0.99, indication strong predictive capability. Using entropy-based analysis, two potential wells were identified for reactivation for improvement. These wells were further evaluated using Chan Diagnostic and Production Performance analysis.
Optimization of CO2 Injection Through Cyclic Huff and Puff to Improve Oil Recovery Dedi Kristanto; Hariyadi Hariyadi; Eko Widi Pramudyohadi; Aditya Kurniawan; Unggul Setiadi Nursidik; Dewi Asmorowati; Indah Widiyaningsih; Ndaru Cahyaningtyas
Scientific Contributions Oil and Gas Vol 48 No 2 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i2.1659

Abstract

One of the Enhanced Oil Recovery (EOR) strategies in the petroleum industry is CO2 injection using the huff and puff method. The method is performed on one well that acts as an injection and a production well. The method works by injecting a certain volume of carbon dioxide (CO2) gas into the reservoir and then closing the well for a period of time. This injection cycle can take place over several cycles. Production can be carried out after one or more cycles according to the design. In this study, CO2 injection optimization with the huff and puff method is carried out with reservoir simulation (GEM-CMG) by taking data from one of the oil and gas wells in Indonesia, with carbonate rock characteristics that are water wet. The simulation work steps include inputting data (fluid, rock properties, and production), initialization, history matching, and CO2 injection optimization with the huff and puff method. The optimization scenarios include optimization of injection pressure and number of cycles. The injection pressure scenario uses a range of 500 - 3000 psi, based on the simulation results obtained that the injection pressure of 500 psi produces the highest recovery factor (RF) of 22.2%. Then, the cyclic scenario was carried out at the optimum injection pressure (500 psi) with the number of cycles 2 - 6 cycles. From the simulation results, it is found that the number of cycles for this carbonate reservoir condition does not have a significant effect, as evidenced by the RF values ranging from 22.1 - 22.3%.
Co-Authors Aditya Kurniawan Boni Swadesi Damar Nandi Wardhana Dedi Kristanto Dewi Asmorowati Eko Widi Pramudyohadi Geovanny Branchiny Imasuly Herlina Jayadianti Indah Widiyaningsih Indah Widiyaningsih Ndaru Cahyaningtyas Ndaru Cahyaningtyas Sayoga Heru Prayitno Unggul Setiadi Nursidik