Integration of Artificial Intelligence and Lean Sigma for Large Field Production Optimization: Application to Kern River Field

Abstract

Abstract This paper represents an integration of artificial intelligence and lean sigma techniques to achieve large field production optimization.The first part of the methodology (detailed in SPE 90266 "Zonal Allocation and Increased Production Opportunities Using Data Mining in Kern River"[1]) involves data management and predictive data mining for increased production opportunity identification.It utilizes a set of data mining tools including clustering techniques and neural networks to identify new candidates for clean-outs, perforating, sidetracks, deepening, and other types of workovers.Furthermore, the expert system was used to predict the estimated production increase for these candidates.The second part of the methodology optimizes the implementation and post-workover follow up of the opportunities identified in part one.It involves the use of lean sigma tools such as value stream mapping, level loading, continuous flow production, standard operating procedures, and kanbans which optimize execution cycle time, peak oil production, decision making process, cost, and safety[2].This approach was successfully applied and executed in the Kern River field. Introduction With over 8,600 active producers averaging 10 BOPD each and a limited staff, streamlining the well optimization process in the Kern River field is critical to take advantage of a large and dynamic portfolio of relatively low oil gain opportunities.It is essential to effectively identify, prioritize, and implement a high number of these opportunities, which typically range from 2 to 8 incremental barrels of oil per day. As detailed in SPE paper 902661, a significant production increase opportunity was discovered in the lower sands through the use of artificial intelligence tools after observing that some wells in the field have high production, while nearby neighbor wells are very low producers.A pilot program was implemented and following its success, the study was extended across the entire field.After identifying the field-wide opportunity, a significant workover program was launched. A lookback on the pilot program indicated several processes, including candidate selection, were successful and would continue to be used "as is" in the execution of the field wide effort.The post-workover follow up and put on production (POP) processes, however, were identified as weaknesses and were highlighted as areas of improvement.Lean Sigma techniques were selected to optimize and streamline these processes. Background This paper represents an integration of artificial intelligence and lean sigma techniques to improve workflow processes and execution of a large field optimization project in Kern River. Reservoir Description.The Kern River field, located in Kern County, California, is a heavy oil reservoir consisting of nine productive sand intervals and many more individual sand lobes or flow units within the Kern River series.The field is 4 miles by 5 miles in areal extent and has over 8,600 active producing wells and 1,200 steam injectors.Producers are co-mingled with very little individual zone production test data available.The field is currently produced by steam injection with varying degrees of thermal maturity in each of the sands.The primary production mechanism is gravity drainage with extremely low average reservoir pressure of 20 psi in the oil sands, requiring pumps to be set at or below the bottom-most oil sand and pumped off to effectively produce. The northeast half of the field has little to no water impacted sands, while the lowermost sands in the central portion of the field are water/aquifer impacted.The water impacted sands are found progressively higher moving southwest, down structure, across the southwest half of the field.Higher pressures exceeding 50 psi are found in these sands.