Most of the mature fields in the United States have been producing for many years. Production in these fields started at a time when reservoir characterization was not a priority; therefore they lack data that can help in reservoir characterization. On the other hand to re-vitalize these fields in a time that price of hydrocarbon is high, requires certain degree of reservoir characterization in order to identify locations with potentials of economical production. The most common type of data that may be found in many of the mature fields is production data. This is due to the fact that usually production data is recorded as a regulatory obligation or simply because it was needed to perform economic analysis.

Using production data as a source for making decisions have been on the petroleum engineer's agenda for many years and several methods have been developed for accomplishing this task. There are three major shortcomings related to the efforts that focus on production data analysis. The first one has to do with the fact that due to the nature of production data its analysis is quite subjective. Even when certain techniques show promise in deducing valuable information from production data, the issue of subjectivity remains intact. Furthermore, as the second shortcoming, existing production data analysis techniques usually address individual wells and therefore do not undertake the entire field or the reservoir as a coherent system.

The third short-coming is the lack of a user friendly software product that can perform production data analysis with minimum subjectivity and reasonable repeatability while addressing the entire field (reservoir) instead of autonomous, disjointed wells. It is a well known fact that techniques such as decline curve analysis and type curve matching address individual wells (or sometime groups of wells without geographic resolution) and are highly subjective.

In this paper a new methodology is introduced that attempts to address the first and the second, i.e. unify a comprehensive production data analsysis with reduced subjectivity while addressing the entire reseroivr with reasonable geographic resolution. The geographic mapping of the depletion or remainind reserves can assists engineers in making informed decision on where to drill or which well to remediate. The third shortcoming will be addressed in a separate paper where a software product is introduced that would perform the analysis with minimum user ineraction.

The techniques introduced here are statistical in nature and focuses on intelligent systems to analyze production data. This methodology integrates conventional production data analysis techniques such as decline curve analysis, type curve matching and single well radial simulation model, with new techniques developed based on intelligent systems (one or more of techniques such as neural networks, genetic algorithms and fuzzy logic) in order to map fluid flow in the reservoir as a function of time. A set of two dimensional maps are generated to identify the relative reservoir quality and three dimensional maps that track the sweet spots in the field with time in order to identify the most appropriate locations that may still have reserves to be produced. This methodology can play an important role in identifying new opportunities in mature fields.

In this paper the methodology is introduced and its application to a field in the mid-continent is demonstrated.

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