Well Performance Improvement By Identifying And Preventing Liquid Loading Using An Integrated Asset Operation Model Framework For Gas Condensate Wells

Abstract

Liquid loading is one of the biggest challenges in operating gas condensate wells of a depletion reservoir. Analyzing this bottleneck using physics-based model estimations is one of the key methodologies in an integrated digital production system, which can help users to take preventive actions, thereby saving cost, time, and efforts considerably. This paper demonstrates the identification of the liquid loading condition in existing producing gas condensate wells using the analytical tools and automated workflows in an integrated framework.

The analytical workflow to investigate liquid loading bottlenecking requires calibrated and representative well models as a first step. These representative well models incorporate a tuned compositional PVT model as a fluid parameter model, and such model outputs match with wells deliverability and historical production trends. Subsequently, the calibrated models are then integrated into a digital platform consisting of automated well analysis workflows. Along with various well performance parameters being analyzed, two key parameters for liquid loading debottlenecking, such as critical unloading velocity and the In-situ velocity, are investigated in the IAOM (Integrated Asset Operation Model) system for each well as the function of depth along well's completion. Furthermore, advanced dashboards present the analysis output in an instructive manner, driving user's engineering judgment to take preventive decisions. The Integration of framework with various corporate data sources provides a continuous stream of representative data that is utilized to estimate wells deliverability under the pre-defined operating envelope.

As a result of the analysis, gas condensate wells that are suffering from liquid loading were identified using the integrated digital framework. Based on the production history and target monitoring, it was observed that these wells were unable to produce as their expected objectives. Identified wells were run through the production gas rate sensitivity analysis using the analytical tool, and as an outcome, the optimal production rate was calculated. Producing the well below this critical rate causes the In-situ velocity to drop below critical unloading velocity. Additionally, using the tuned and calibrated network model, the operating choke was identified to maintain the stable flow in the well and avoid further liquid loading. This choke size was provided to field operation for implementation and, thus, saved the cost and man-hour spent during the flowing gradient surveys. The case study demonstrates significant production improvements observed for these wells, thereby assisting the operator in reducing cost and time significantly.

Using Integration of latest production optimization platforms not only provides tools to identify wells which are currently experiencing liquid loading problem but also healthy wells which might come across liquid loading problem in the course of production, thus helping in taking proactive remedial action. Furthermore, the integrated framework provides erosional velocity related data, which acts as a guideline while optimizing gas production.