Accomplishing Long-Term Sustainable Well and Reservoir Health by Employing a Statistical Methodology for Choke Size Estimation in an Integrated Asset Model

Abstract

One noteworthy challenge in a giant field with limited real-time information is the prediction of choke settings for regulating production rates to maintain a long-term sustainable production in the field. This paper defines a forecast analysis method to determine the associations between liquid rates, wellhead pressure and choke tuning factor to enhance the preceding method of fixed choke tuning factors in the network model.

An integrated asset model provides choke tuning factors based on measured parameters, such as wellhead pressure (WHP), liquid rate and wellhead temperature for corresponding choke size during each well-test measurement. Considering the interest of producing the wells to a target rate, a correlation between the well measurement and the tuning factor was envisioned.

A large volume of historical data was used to determine the relationship between choke tuning factor and liquid rate. A set of curves with corresponding algorithmic regressions were determined, grouped by the product of wellhead pressure and choke size, and used to calculate new choke tuning factors for improved accuracy of estimation. These correlations were coded to improve user-experience and nurture integration to other related workflows, such as: running forecasting scenarios.

This modified approach improves the choke estimation and reduces the impact of choke size over-prediction and under-estimation in contrast with a fixed-choke tuning-factor approach.

This estimation positively aids in delivering the key responsibility of the newly formed Production Optimization team, i.e. to deliver the target production without affecting the reservoir health. Major drawbacks in the earlier approach were overestimation of choke-sizes, causing guidelines violations and under-estimation of choke size, resulting in the inability to meet the production commitment. This comprehensive method resolved such problems.

The regression analysis and integrated network model provides a realistic choke estimation. As the previous approach needed significant manual intervention caused by unrealistic choke estimations, this iterative approach reduced the need for more than 95% cases and improved the calculation efficiency by 90%.

Considering the relatively low percentage of the field instrumentation at well level, the choke size estimation is the only way to control the well and to advise operation on the required target rate. This unique approach simplified and facilitated the multiple key business and production efficiency improvement (PEI) scenarios such as maximum field potential (MFPR), field technical rate (FTR), field capacity test (FCT) and well target setting scenario.

As the low oil price environment has affected the instrumentation of the oil industry as a whole, this approach provides a better way of optimizing and controlling the well production by improving choke size estimation and with minimal chances of estimation errors.