The preferred method of reserves evaluation for producing wells is deterministic. However, when deterministic methods are used, evaluators cannot reliably assign reserves that satisfy the prescribed certainty for each reserves category. In addition, they have no method to quantify the impact that aggregation will have on reserves certainty.

Deterministic reserves estimates are single-valued and may result from analogy, reservoir simulation, decline analysis, or other analytical methods. Because the certainty associated with deterministic reserves assignments are unknown, the norm is that the 2P (proved plus probable) reserves are the evaluator’s best estimate, and the other categories are the evaluator’s best judgment for a high and low estimate. This standard defeats the objective of consistency between evaluators.

When an evaluation is for a group of wells, the reserves certainty for the group will increase and the assigned reserves will be closer to the mean. Therefore, the 1P (proved) and 2P reserves for the group will exceed the sum of the 1P and 2P reserves for individual wells within the group. This aggregation benefit of increasing 1P and 2P reserves is not available for wells with deterministically calculated reserves because evaluators require probability distributions to make the calculations.

The objective of this paper is to demonstrate how evaluators may overcome these deficiencies and improve the accuracy of their reserves reporting through a new method of statistically enhanced decline analysis (SEDA).

Our method was to identify a statistical distribution from which we could assign reserves. We chose to work with a statistically significant number of older analogous wells with reliable best estimate reserves. Using subsets of the available production history (usually annual increments), we forecasted using truncated data and compared that to the remaining reserves using all data. The result of the comparison was the remaining reserves ratio (RRR) that is the ratio of the remaining reserves, calculated using truncated data, to the best estimate of remaining reserves using all data.

With Monte Carlo simulation, we created a probability distribution of RRR for a group of specified size and production life. 2P reserves occur when RRR = 1.0. We transformed the best estimate to 2P, and achieved certainty by shifting the probability distribution so that RRR would equal 1.0 at 50 percent probability. For a specified certainty, we obtained the RRR from the adjusted distribution and multiplied it by the 2P reserves to obtain the reserves for that certainty.

Evaluators now have a method for accurate, consistent reporting of deterministic reserves. Productivity improves because 1P and 3P (proved plus probable plus possible) are automatically determined through the SEDA process.

To our knowledge, SEDA is the only method available to resolve deficiencies of deterministic reserves estimates. The method adapts well to existing reserves evaluation software and improves accuracy and consistency at reduced cost.

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