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Tiutiaev, Andrei; Dolzhikova, Irina; Komarova, Olesya; Dolzhikov, Andrei; Makarov, Dmitriy
International Multidisciplinary Scientific GeoConference : SGEM, 01/2020, Volume: 20, Issue: 1.2Conference Proceeding
This research describes such a problem as increasing the productivity of wells and the oil recovery coefficient by conducting various geological and technical measures, which based on correlation, regression, and statistical analyzes. These methods allow to analyse a possible interconnection of two or more numbers of indicators, also identifying the problematic well stock, and selecting a set of measures aimed at improving the efficiency of field development, including various geological and technical measures. Existing systems for processing and interpreting field, geophysical, geological, petrophysical and seismic information allow monitoring development, and effective planning of technical measures makes it possible to optimize development. The most effective planning methods at the moment are methods, the most important element of which is the analysis of the current geological and technological characteristics of the developed objects. A sound analysis requires full-scale modeling, but the processing of large databases is associated with significant labor costs. In this regard, it seems possible to use correlation and regression analysis, allowing to analyze large volumes of available data from previous experience in various processes. The main advantage of this statistical analysis is the simplicity of calculation using software packages like Excel and Statistica. In addition, this approach is also applicable when analyzing the effect of various complications on the efficiency of downhole equipment, for example, the dependence of the overhaul period on the content of solids, paraffins, high gas factor, etc. The most common methods of statistical processing of field data at the moment are both correlation and regression analysis. These are two highly effective methods that allow analyzing large volumes of data to study the possible relationship between two or more indicators. The main advantage of this statistical analysis is the simplicity of calculation using software packages like Excel and Statistica. Examples of this approach are shown.
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