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

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.

Combined artificial intelligence modeling for production forecast in a petroleum production field

“…In [34], a genetic optimization algorithm is used to evaluate different scenarios for several gas producer fields, resulting in a solution to the proposed production problem. The work [35] integrates AI with the six-sigma norm to adjust oil production according to market demand. Also, using classical optimization algorithms, in [36], an optimal operation is found using numerical models and ANN.…”

Advanced Supervision Of Oil Wells Based On Soft Computing Techniques

“…For that, it determines the production rate (see Figure 4.C), and according to this value it can identify the operational scenario. In this case the set of rules are defined by the bottom pressure (P w f , fluid load capacity of the reservoir) and the gas injection rate (Q in j , energy needed to extract the oil), because these variables determine the production rate according to [7], [8]. With the output of the last level (Q prod , the production rate) the HIS determines the operational scenario of the well.…”

“…For that, it determines the production rate (see Fig 4.C), and according to this value it can identify the operational scenario. In this case the set of rules are defined by the bottom pressure (P wf , fluid load capacity of the reservoir) and the gas injection rate (Q inj , energy needed to extract the oil), because these variables determine the production rate according to [7], [8]. And the restrictions of the process are: we assume that: P ws is a constant, due to the slow dynamics of the reservoir; and P w f is lower than the pressure of the reservoir, due to the fact that in a well the pressure of bottom is minor that the pressure of reservoir.…”

Towards Sustainability Through Green, Lean and Six Sigma Integration at Service Industry: Review and Framework

“…Six Sigma was created in the 1980's by Bill Smith at the Motorola Corporation, and seeks to reduce errors and defects by applying the DMAIC (Define, Measure, Analyze, Improve, and Control) methodology. Six Sigma is a highly disciplined process that helps organizations focus on delivering lower-cost products with improved quality and reduced cycle time, where Sigma represents a statistical term that measures the extent to which a given process deviates from perfection (Popa et al, 2005).…”

“…Lean Six Sigma (LSS) is a methodology of process improvement used in organizations of international standard in order to eliminate waste in the processes and deliver products and services with extreme quality to its clients (Popa et al, 2005). Furthermore, LSS can be considered a broad well-structured, systematic, strategic, integrated and long-term decision-making approach to improve quality, cost, speed, delivery and customer satisfaction performance that focuses on reducing variation in critical processes to achieve bottom-line benefits through merger of tools and principles of Lean and Six Sigma and enables organizations to meet and exceed customer expectations in a competitive global environment (Ray & John, 2011;Laureani & Antony, 2012;Nicoletti, 2013;Andersson, Hilletofth, Manfredsson, & Hilmola, 2014;Gutierrez-Gutierrez et al, 2016).…”