Muhammad Ananda Trisnadi

• Performed calculations and analyses to optimize reservoir and production engineering
• Applied data science, AI, and machine learning to integrate personnel, processes, and technology, leveraging real-time and non-real-time data
• Projects Include:

AI-Driven Low Quality Reservoir Classification & Perforation Optimization in a Giant-Mature Waterflood Field

• Results: Collaborated with geoscientists and engineers to integrate subsurface data into deep-learning models for automated play-type classification and water-cut prediction, enhancing reservoir characterization in a 2,000+ well field with 99.4% water cut
• Improved classification accuracy to 88% for play types and achieved MAEs below 25% in water-cut prediction
• Impact: Optimized perforation recommendations, reduced interpretation bias, and enabled large-scale reservoir analysis, improving drilling and workover success rates
• Technologies Used: Python, Deep Learning (UTime, LSTM, 1D-CNN), Well Log Data Analysis

Pump Job Recommendation Automation Based on Dyno Pattern Interpretation

• Overview: Automated rigless job decision-making through dyno pattern analysis, accurately assessing pump conditions and correlating with reservoir/wellbore issues
• Results: Elevated decision-making reliability, moving beyond traditional single-parameter evaluations
• Technologies Used: Machine learning algorithms, data integration frameworks

Basic Sediment & Water (BSW) Prediction in Heavy Oil Steam Flood Central Gathering Station

• Impact: Achieved continuous BSW compliance since deployment in Sep 2024, mitigating potential operational risks and downtime
• Overview: Developed a predictive machine learning model to forecast BSW levels 4-24 hours in advance, supporting proactive management and regulatory adherence
• Technologies Used: Python, machine learning, data visualization tools

• Conducted Integrated Review of 19 fields, evaluated reservoir and well data to identify workover candidates; executed 117 incident-free jobs, resulting in a daily production gain of approximately 4,000 barrels of oil
• Improved reservoir management practices through high resolution stratigraphic framework, workover lookbacks, optimizing production strategies and well completion techniques
• Artificial lift optimization: selected pump type and designed fluid rate based on well performance for each workover well, avoiding critical rate for water conning or gas bubbles to occur
• Developed cost-optimized workover programs in collaboration with completion engineers, improving job efficiency and cost-effectiveness
• Designed and optimized fracturing campaigns, overseeing job design, execution, and evaluation for improved operational outcomes
• Reviewed and analyzed new zone behind pipe (NZBP) candidates, conducting production forecasting, recommending well completions, and performing economic analysis for optimal reservoir management

• Led the development annual OEB Planning of $50+ MM for the Sumatera Light Oil Operation Asset Optimization team based on historical data, rig forecast, and allocation of job type forecast
• Delivered comprehensive monthly OEB reports, presenting key insights on forecast assumptions and reliability to senior management for endorsement and decision-making
• Managed OEB spending strategy during a low-budget phase in 2020, prioritizing high-value projects and controlling rig-spread costs, resulting in a 12% reduction in spending while maintaining incremental production

• Managed matured gas fields in Central Sumatra with a total production of ~30 MMSCFD with optimization activity: Liquid unloading effort, Workovers, New Zone Behind Pipe (NZBP), Convert to Gas (CTG), and idle well reactivation
• Led optimization efforts for low-pressure and liquid-loaded gas wells, using portable gas compressors to extend production life
• Collaborated with the reservoir modelling and engineering team to design gas well deliverability test for surface facility sizing
• Guided field and gas plant teams to enhance operational performance and maximize production efficiency

• Managed heat requirements for the Duri Steamflood field (DSF), the largest steamflood field globally, with 10,000+ wells, steam injection ~370 MBSPD, and 40,000 barrels/day of oil production
• Led Reservoir Management Review for optimizing steam injection and recovery strategies, providing technical recommendations for steam distribution, injector workovers, and cyclic steam
• Managed the CSS process for the entire heavy oil field, overseeing job selection, execution, and incremental oil forecasting
• Completed 4,800+ CSS jobs in 2 years, increasing oil production by up to 3,000 barrels/day, within a $2MM OPEX budget
• Pioneered a 300-job/month activity level, ensuring safe operations and efficient coordination, presenting the success to the Vice President of Asset Development

• Monitored daily production and injection, well down, workover, and well service activities that are under execution for 263 producers, 90 Injectors, with production 8.8 MBOPD and injection 1,318 MBPD, with ~150 jobs of WO/WS activities per year
• Performed routine field & well review in Area-3 aligning with overall Minas Field Reservoir Management Strategy, to get strong candidates for WO backlog and maintain production decline
• Lead Engineer of optimization effort for 'Revise Packer' job candidate selection process improvement in Minas Full Field scale
• Result: 40% of oil gain improvement (5 BOPD improvement) after full implementation of new selection criteria and new recommended job procedure

• Led Heat Management Review for designated areas, implementing Chevron Heat Management practices and workflows to optimize reservoir recovery, steam distribution, and cyclic steam stimulation plan
• Reviewed candidates for the regular and connecting to steam chest type of Cyclic Steam Stimulation well program

• Facilitated discussion section or tutorial, prepared student task, graded homework, programmed assignments & exams, and prepared answer keys or supplementary notes
• Lecturer: Dr. Ir. Arsegianto

• Mapping remaining oil in Minas full field by generating Decline Curve Analysis for each of ~1400 production well and used the map for determining sweet spot area for new wells drilling location