Accurate Gas-To-Pump Calculation in Pumping Oil Wells from Surface Data Using Combined Analytical Modeling of Gaseous Static Liquids Gradient: Part 1

Abstract Among the many available methods for determining pump intake pressure and flowing bottom hole pressure in pumping wells, there remains the practical need to both reduce the input field data modeling requirements (carbon and cost reduction) and to combine the different but related concurrent, countercurrent and column multiphase flow phenomena governing the calculation (accuracy improvement). This paper furnishes both lab- and field-validated analytical multiphase modeling methods showing the various ways the discovered triangular interrelationship between pump intake pressure, gaseous static liquid level and downhole gas separation efficiency changes in response to different sensitivities. The pressure distribution along the entire multiphase flow path of the pumping oil well, including between the pump intake pressure and flowing bottomhole pressure at reservoir depths, is also modeled in detail. A notable difference in this work in reference to prior works of pump intake pressure and gas-to-pump (i.e., gas holdup at pump intake region) modeling is a more detailed physics-based understanding of how gas holdup changes and develops along the gaseous static liquid column above the downhole packer-less pump. In this way, using an easy-to-compute, zero-cost, independently reproducible, published model for bubbly to churn flow in combination with a cutting edge commercial analytical multiphase flow simulator, we first validate the simulator results with published lab datasets of developing gas flow through static liquid columns under carefully controlled conditions. Then, several published field datasets of producing oil wells with liquid levels are simulated to confirm the extensibility of our model to actual field wells and currently-active production operations. In these field validations, the transient countercurrent liquids loading feature of the simulator is utilized to determine the prevailing liquid level. We then additionally perform several important sensitivities showing the various ways that pump intake pressure, flowing bottomhole pressure, gaseous static liquid level, and downhole gas separation efficiency changes in response to different hydraulic diameters, flowing areas, casing-annulus clearances (e.g., ESP versus rod pump), liquid column flow patterns, axial developing flow lengths, and wellbore inclination. Regarding our liquid level buildup simulations, we demonstrate the effect that liquid levels have on dictating the possible operating limits on highest and lowest downhole gas separation efficiencies. This work represents a step change in our understanding of the aspect of multiphase flows that is most pertinent to artificial lift: accurate critical gas velocity prediction leading to reliable modeling of countercurrent multiphase liquid loading and gas flow along static liquid columns. We lay the foundation for a change in conversation among the artificial lift community for paying much more practical attention as well as research interest into the multiphase countercurrent and gaseous static liquid column flow behaviors prevalent in the majority pumping oil wells and liquids loaded gas wells. To this end, a new industry digital computing capability is presented and comprehensively validated in both this paper and the part-2 paper of this paper series (Nagoo et al., 2022a): the ability to perform multiphase countercurrent liquid loading simulations that dynamically loads a pumping oil well casing-annulus or gas well tubing/casing, and the reliable calculations of the total pressure gradient that varies with the increasing gas holdup along the static liquid column of these wells. This means that for pumping oil wells (SRP, ESP, PCP, etc.), the pump intake pressure, flowing bottomhole pressure at reservoir depths, downhole gas separation efficiency and static gaseous liquid level above the pump can now be simultaneously simulated from only basic surface field data. For the part-2 paper, our new method is used to calculate the static gaseous liquid levels in liquid loaded gas wells from only basic surface field data. In terms of digital twin applications for the oilfield, our new methods can be performed in real-time in an autonomous way on an IIOT-enabled (IIOT = industrial internet of things) wellhead device to continuously optimize production to create value at scale. We term this device a "wellbore liquid level digital sensor": a solution that takes in real-time SCADA (supervisory control and data acquisition) surface data and converts it to automated calculations of pump intake pressure, flowing bottomhole pressure, downhole gas separation efficiency, gaseous static liquid column height and gas holdup profile along the static liquid column. This is an industry-first, at-scale, digital oilfield solution purpose built for low-carbon downhole diagnostics and real-time autonomous production optimization calculations. Such calculations are used to drive the real-time production operations decisions needed for minimizing lifting costs and minimizing unplanned shut-downs and pump/equipment failures. Indeed, a net-zero future for the oil and gas industry on a whole relies on the digital innovations like those provided in this large body of work to empower energy transformation, and to manage/harness the immense amount of asset data in ways that was not possible before. Incorporating novel digital solutions in day-to-day oil and gas production operations will improve engineer productivity (higher value creation) as well as corporate profitability (safer, lower-carbon operations) by streamlining downhole calculations, analyses, operational performance indicators, and cutting costs for better decision-making support.

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