Intelligent Production Data Analysis - IPDA, is a new methodology for Reservoir Characterization based only on monthly production rate data. This technique combines conventional methods of production data analysis (decline curve analysis, type curve matching and history matching) with intelligent systems. The study targets the validation of this methodology under a controlled environment, attempting three main objectives: Identifying Sweet Spots, Forecasting Reserves and recognizing under-performer wells.
The study investigates the behavior of five different reservoirs, modeled using a commercial simulator. The structure, parameters and heterogeneity of each configuration was inspired by existing formations. Records of production rate data were generated from the simulated fields (both single and multi-layer formations) and used as input to perform an "Intelligent Production Data Analysis".
The findings highlight strength of this technique in tracking the fluid movement in the reservoir as a function of time. Furthermore, this study identifies some limitations and circumstances under which the analysis may not result in correct recommendations.
The recent rise in the global demand for energy has significantly increased oil and gas prices. In the last few years E&P companies have reported record profits. A new reality rules the energy market, and large amounts of money have been invested in order to increase the production capacity. Now, mature fields, which were not profitable in the late 90s, have become very attractive for major oil and gas producers. An effective revitalization of this type of reservoirs has come to play a big role in the industry.
Recovery techniques have been tremendously improved over the last decade. However, lack of data is a problem with mature fields. Production Rate is about the only data that can be easily accessed in most of the brown fields. But, what can be done with this data?
Recently, a new technique for production analysis was introduced. The procedure is called Intelligent Production Data Analysis (IPDA). It combines the well-known methods for production data analysis (Decline Curve Analysis, Type Curve Matching and History Matching) with intelligent systems (Neural Networks, Genetic Algorithms and Fuzzy Logic). The results provide a unified set of reservoir characteristics based only on records of monthly production rate data. The fact that this information can be found in public records, projects IPDA as a valuable tool for independent asset evaluation, prior to lease acquisitions.
The IPDA technique has been applied in several fields throughout the United States (Rockies and Mid-Continent). One of these cases was the Golden Trend fields in Oklahoma 1. The only available data in this field was monthly production rate data. The application of IPDA technique provided both reservoir intrinsic properties and remaining reserves distribution throughtout the field.
In 2006 Jalali applied IPDA technique to characterize Carthage Field in the Cotton Valley Formation in Texas. Production records from 349 wells were employed during this analysis. The result was a unified set of reservoir indicators such as, Estimated Ultimate Recovery (EUR), remaining reserves at different times, permeability distribution, drainage area, etc.