The highlight of this year’s feature on mature fields and well revitalization has been the increasing use of artificial intelligence (AI) and machine learning (ML) for analysis of mature assets. Many paper proposals studied using AI/ML techniques cover a wide range of topics including optimization of production operations, faster reservoir characterization of mature fields, and improvement of ongoing waterfloods. In fact, increased use of AI/ML has been a recurring theme in many of the areas covered in the Technology Focus sections of 2021’s JPT issues. There has also been a focus on implementing novel technologies such as the use of autonomous inflow-control devices in horizontal wells to suppress ingress of unwanted fluids in thin-oil-rim reservoirs, using integrated-asset models for debottlenecking, and the use of cased-hole logging in old wells to look for missed opportunities.
After serving for 5 years, my time for writing this column is coming to an end. It has been a highly rewarding exercise going through the paper proposals, experiencing the hard work put in by industry professionals to arrest inevitably declining production from mature assets by putting together ideas in diverse fields such as reservoir management, production optimization, drilling, and formation evaluation to produce the extra barrel from mature brownfields.
Out of the 314 paper proposals studied in these 5 years, 137 (44%) dealt with waterflood improvement, reservoir characterization, and simulation, and approximately 78 papers (25%) dealt with well revitalization involving workover drilling, completion, and stimulation. Sustaining production from mature brownfields challenges the best from each discipline, and significant success stories have come from a genuine collaboration of ideas across disciplines from subsurface to engineering.
This Month’s Technical Papers
Low-Resistivity/Low-Contrast Reservoirs Increase Production in Offshore Brownfield
Self-Organizing-Map Approach Characterizes Naturally Fractured Reservoir in a Mature Field
Extended-Reach Wells Help Optimize Production in Marginal Offshore Prospects
Recommended Additional Reading
SPE 206195 Data-Driven Optimization of Intermittent Gas Production in Mature Fields Assisted by Deep Learning and a Population-Based Global Optimizer by Javier Fatou Gómez, TNO, et al.
SPE 205546 Role of Geomechanics in Identification of Possible Mechanisms for Nonproductive Time and Improving Drilling Operations in a Mature Field in Offshore Sarawak, Malaysia by Avirup Chatterjee, Baker Hughes, et al.
SPE 206303 Combining Capacitance Resistance Model With Geological Data for Large Reservoirs by Davud Davudov, Resermine, et al.
R.V. Marathe, SPE, is a reservoir-engineering consultant. He worked for Oil and Natural Gas Corporation (ONGC), the national oil company of India, from 1978 to 2014. Marathe’s last position at ONGC was as executive director and asset manager, Mumbai, for the largest oil-producing asset of India. He was head of ONGC’s Institute of Reservoir Studies for more than 5 years. The institute is responsible for improved and enhanced oil recovery and developmental activities for all fields operated by ONGC. Marathe has presented papers at various SPE conferences on oilfield development, reservoir simulation, modeling of laboratory studies, and pressure-transient analysis. He holds a PhD degree in applications of potential theory to petroleum reservoir engineering from the Indian Institute of Technology Bombay. Marathe is a member of the JPT Editorial Review Committee and can be reached at maratherv@gmail.com.