Proper and reliable resource assessment of hydrocarbons in-place and recoverable volumes is one of the key factors in field development planning (FDP) and determines the commitments made to the host government for the reserves to be developed (RTBD). Many times, it is critical to update the resources and reserves of a producing asset through full field reviews (FFR) to gauge the production attainment and success of initial forecasts in FDP and also to locate any upside/locked-in potential.
Often uncertainties in the field development are expected to reduce as the field produces, but in many cases the results show otherwise due to lack/ inaccuracy of data or existing reservoir complexities. This paper elaborates how an integrated approach utilizing analytical methods (material balance, pressure and rate transient analysis) combined to numerical reservoir simulation is used for accurate resource assessment of an over-pressured gas condensate reservoir that suffers from lack of geological and petrophysical data, faulty production data measurement system and complex fluid and pressure behavior.
A comprehensive workflow comprising of different methodologies is used to harness the available geological, petrophysical, production and pressure data. Over-pressured and compressibility corrected gas material balance and pressure and rate transient analysis (RTA) are conducted using static and flowing data to encompass the existing uncertainties on resource numbers and generate low, base and high cases. The results of these methods are then successfully utilized to construct the dynamic reservoir model for evaluation of the upside and near field exploitation (NFE) potential. The results of the full field review lead to a 50% increase in the gas initially in-place compared to FDP volumes and a significant addition in the proven reserve. This increase in volumes was investigated through proactive surveillance for a period of time and was well supported by the reservoir and well performance.
A novel approach to numerically model the over-pressured gas reservoirs is developed using a simple concept of compressibility modifications supported by production data history match and analogue core data. The results of the study greatly benefited the production sharing contract (PSC) and lead to production enhancement from the field through a proper debottlenecking project.
Uncertainty and risk analysis is an inseparable part of any decision making process in the field development planning. This study sheds light on the available approaches to capture the range of uncertainties but digs deep into the misuses of the probabilistic approach that renders the method difficult and time consuming to implement with little added value for risk mitigation and proper decision making.
Probabilistic modeling using dynamic simulation models has been adopted in recent decades to address the variations in forecasted production profiles and to capture the uncertainties. However, there are misuses in the approach that pose questions on the outcome and its meaningfulness. Lack of enough spread in the forecast, history-matched models with physically incorrect parameter ranges/ combinations and models suggesting contradicting development scenarios are among examples. These in turn make the probabilistic forecasting output inconclusive and considering the high computational cost and time required to perform the exercise makes it unattractive to management. In this paper four case studies including mature and green fields have been described and a number of main issues and pitfalls of using probabilistic dynamic modeling in those cases are analyzed. General workflows are then presented for green and brown fields based on experimental design, proxy modeling, optimization and prediction candidates selection that provides solution for proper selection and implementation of the probabilistic dynamic modeling.
It is argued that probabilistic modeling can help better capture the uncertainties and reduce the risk in field development planning provided that a fit-for-purpose approach is taken with correct understanding of the data requirement according to the reservoir complexity, the physical processes being modeled and assumptions used in the methodologies and simulation engines. This is in contrast to the attempts to capture the ranges of recoverables based on deterministic high and low cases that is often inefficient as the optimistic high-case of ‘hole-in-one’, may suggest an ideal but not plausible scenario whereas the pessimistic low-case of ‘train-wreck’ may be economically unattractive. The exercise then leaves the companies with the best technical estimate model to make the final call and the numbers from other models are only used for reserve booking purposes.
The published papers in the literature include discussions on deterministic vs. probabilistic approaches and selection of base case models, the detailed algorithms and also case studies done using the published methods available in the commercial softwares. This paper however discusses the misuses of the probabilistic dynamic modelling approach and tries to inform the audience of the pitfalls of not understanding the reservoir and/or the tools used in implementing the methods and in this sense it is novel.