Optimizing Artificial Lift Timing and Selection Using Reduced Physics Models

Abstract

Unconventional field production relies heavily on artificial lift, but with reservoir energy depleting, lifting hydrocarbons efficiently and economically is one of the challenging parts of field development. Traditional lift selection methods are insufficient for managing unconventional wells with high initial decline rates. Understanding how production behaves under various lift conditions is crucial because lift method timing and design are the most important considerations for optimizing well performance. In order to increase the value of unconventional oil and gas assets, this paper presents an artificial-lift timing and selection (ALTS) methodology that is based on a hybrid data-driven and physics-based workflow. Our formulation employs a reduced physics model that is based on identification of Dynamic Drainage Volume (DDV) using commonly measured data (flowback, daily production rates, and wellhead pressure) to calculate reservoir pressure depletion, transient productivity index (PI) and dynamic inflow performance relationship (IPR). Transient PI as the forecasting variable normalizes both surface pressure effects and takes phase behavior into account, reducing noise. For any bottom hole pressure condition, the PI-based forecasting method is used to predict future IPRs and, as a result, oil, water, and gas rates. The workflow calculates well deliverability under various artificial lift types and design parameters. The ALTS workflow was applied to real-world field cases involving wells flowing under various operating conditions to determine the best strategy for producing the well among several candidate scenarios. The results of transient PI and dynamic IPR provided valuable insights into how and when to select different AL systems. The workflow is run on a regular basis with ever-changing subsurface and wellbore conditions against each candidate scenario using different pump models and other operating parameters (pressure, speed etc.). The method was applied in hindcasting mode to several wells to evaluate lost production opportunity and validate the results. In some cases, the best recommendation was to use a different artificial lift system than the one used in the field to significantly improve long-term well performance. Furthermore, optimal artificial lift operating point recommendations for wells are made, including optimal gas lift rates for gas lifted wells, optimal pump unit selection and speed for ESP and SRP wells. The proposed method predicts future unconventional reservoir IPR consistently and allows for continuous evaluation of artificial lift timing and selection scenarios in unconventional reservoirs with multiple lift types and designs. This has the potential to shift incumbent practices based on broad field heuristics, which are frequently ad hoc, inefficient, and manually intensive, toward well-specific ALTS analysis to improve field economics. Continuous use of this process has been shown to improve production, reduce deferred production, and extend the life of lift equipment.