Artificial Lift Optimization with Data Based Predictive Approach for Pre-Emptive Changeovers of Jet Pump in Polymer Flooded Field

Abstract

Mangala field is one of the largest discovered group of oil fields in Barmer Basin, Rajasthan, India. The fields contain medium gravity viscous crude (10-40cp) in high permeability (1-5 Darcy) sands. Currently the field is on polymer flood to improve the sweep efficiency during enhanced oil recovery. As expected, polymer breakthrough was observed in producer wells. However, this has resulted in challenging well interventions due to polymer/scale depositions in the wellbore and Downhole artificial lift equipment. This issue has surfaced due to mixing of produced polymer with scales, wax and various bivalent ions. Major concerns due to polymer deposition included, fouling of artificial lift system, decrease of well uptime and decreased efficiency of jet pump (type of artificial lift). Reverse jet pumping, where power fluid is pumped through annulus and production is taken through tubing, is the most common method of artificial lift for the field. During jet-pump redressing, polymer deposition has been observed in the Body X-over (Reservoir liquid path), check valve assembly, throat and spacer nozzle to throat inside jet-pump. Continuous chemical injection was tried and proved to be a technical success, but it was not cost effective. Hence data-based predictive approach for pre-emptive changeovers of Jet Pump was developed. The developed model is "intelligent". It learns from every newly logged event and auto correct its approach of marking the risk levels of a well. The well intervention history of over 1000+ well interventions in 170+ oil producers were recorded. The recorded data was then studied for sensitivities such asnumber of interventions viz. – wire scratcher run and Jet pump retrieval / installation runsObservations on severity of polymer/scaling issueChemical soaking requirement of chelating agents such as EDTA, DTPA for dispersing polymer,Well downtimeHistories of slickline fishing events, stuck BHAs. The data was then analyzed and used to create a well intervention risk matrix which in turn classifies all 170 wells into low, moderate and high-risk wells. This approach also sets a predictive timeline for individual well failures. The model/approach is intelligent enough to learn from operational history and auto-corrects itself every time a new event is logged. This paper addressesFormation of agglomerated polymer lumps due to scale formation inside well completions.Deposition of polymer layer inside completion equipment and production tubingDetailed stepwise analysis of over 1000+ well intervention in oil producers producing oil and polymer mixed water.Basis of logic for creating a predictive sheet for Jet Pump change outs.Predict optimal Intervention frequency for every well to de-risk jet pump change slickline interventionDetermination of critical wells which have severe deposition issuesTracking of rigless units’ efficiency and planning, especially highly mobile slickline unitOptimize production from fieldPlan for chemical soaking in tubing of wells where polymer and scale deposition are predicted. This paper gives a new approach to those E&P companies who are producing their field on Jet pumps and are using Polymer flood as recovery mechanism. The use of this approach from day zero in such fields would help to create a customized analytical approach for the field and hence reduce production downtime.