Analysis of production data for characterization of reservoirs is becoming more popular. Sophisticated tools and methodologies exist to extract permeability, completion effectiveness and pore volume from production data (rates and flowing pressures). Often, in practice, these are the only tools available, especially in the case of low permeability reservoirs where it is not practical to conduct buildup tests.
Production data, though plentiful, can be of poor quality because of operational problems, or changes in operating conditions, which are usually not documented (re-completions, tubing change-outs, compression, liquid loading, pressure or rate averaging). If this poor quality or inconsistent data is not recognized as such, it can easily be misinterpreted as being a reservoir issue rather than the operating problem that it is. For example, liquid loading, if not recognized, may be misinterpreted as interwell interference or reservoir depletion.
In this paper, we review many of the operating problems we have observed in practice, and we discuss ways and patterns for recognizing them as operating issues rather than reservoir phenomena. Diagnosis of production data quality is a critical and necessary step that must precede any interpretation of production data. Recognition of inconsistent data is not always an easy task, and requires a lot of experience. The diagnostics presented in this paper will help the engineer in recognizing these problems, and potentially avoiding misinterpretation of the data.
The analysis of production data to determine reservoir characteristics, completion effectiveness and hydrocarbons-inplace is becoming more and more prevalent. The methods of analysis have been documented and verified in numerous publications [1–7]. The concepts underlying modern production data analysis are the same as pressure transient analysis. Even though both these domains use the same underlying theory of fluid flow through porous media and can determine the same variables (permeability, skin, reservoir size), it should not be assumed that they can replace each other. Pressure transient analysis and production data analysis should be viewed as complimentary and not substitutes for each other. Pressure transient analysis deals mostly with "high frequency/high resolution" shut-in data while production data analysis deals with "low frequency/low resolution" flowing data. This, in itself, presents significant differences in data quality and interpretations.
Like all mathematical solutions, the production data analysis methods are subject to numerous assumptions, which often can be justified. In this case, if the data are complete, consistent and of good quality, meaningful results can be obtained. However, if the quality of the data is questionable, then the production data analysis methods should be used with caution. In this case, the analyst's ability to filter out the bad data and extract the true reservoir signal becomes extremely important. As mentioned by Anderson et al. [8], blind application of production data analysis methods without consideration of data quality issues can lead to misinterpretation of the reservoir characteristics. An analyst, who is not experienced in recognizing such inconsistencies, can obtain an answer that appears to be mathematically correct yet be completely wrong because of using "bad data" for analysis.
