Abstract

This paper describes the history matching and predictive case studies of two deepwater Gulf of Mexico (GOM) fields using an advanced Bayes linear estimation tool. Advantages of the tool include significant acceleration of the history matching process, identification and quality measurements of multiple history matches, quantification of reservoir uncertainty, and an improved understanding of reservoir performance. Additionally, a statistical estimator of predictive simulation results is created to generate statistically valid confidence intervals around performance predictions. This paper describes a practical workflow incorporating this tool to rapidly evaluate deepwater producing gas fields, and illustrates its use to determine remaining field potential and future development requirements of two fields, the Harrier and the Raptor Fields in the Pioneer Natural Resources-operated Falcon Corridor.

Introduction

The deepwater GOM can contain fields with very prolific wells that can be highly profitable for an Operator. The loss of even one of these wells can adversely impact both short and long-term field production forecasts, thus cash flow and profitability. These impacts are especially significant when there are few, very high rate wells that contribute to the total field production. When such a well fails, it is crucial to understand the causes in order to determine how and if the situation can be remedied, the cost necessary to do so, and the risks involved. The goals are then to understand and reduce risks, to minimize cycle time and capital exposure, and to maximize profitability.

If a well’s failure to meet forecast expectations is attributed to reservoir performance, a number of tools ranging from the very simple to the very complex can be used to evaluate reservoir performance. The choice as to which tools to use is dependent upon the amount and quality of data available, the complexity of the problem, the time available in which to make a decision, and the magnitude of the capital required to execute the decision. Often history matching with 3-dimensional (3D) reservoir simulation is the tool of choice used to evaluate and explain production performance. However, the history-matching process can be very frustrating and time-consuming, even for fields that appear relatively simple in nature, because of the reservoir processes involved and the non-unique nature of the solution.[1]  Consequently, much time and many resources can be spent in attempting to achieve even one history match. Frequently multiple solutions can be found that can satisfy history-match criteria but which yield divergent prediction outcomes.

Because of the high production rates in both the Harrier and Raptor Fields, rapid analysis and integration of production data were necessary to provide quick answers to reservoir analysis and reservoir management questions, and to address well-intervention and deepwater rig availability decisions. Pioneer selected 3D simulation and the implementation of an advanced linear Bayesian tool to expedite the history matching and uncertainty analyses process.[2] 3D static geologic models for both fields, built and upscaled for dynamic flow simulation prior to production start-up, had been used for predictive simulations. Good quality pressure information from permanent downhole gauges and daily gas production data were available for the calibration of these models. Although there is a global workflow process that encompasses the ‘seismic to simulation’ process, this paper focuses primarily upon the dynamic history matching and predictive portion of the overall process.