Abstract
The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.
Highlights
Interpretation of well logging data is an important stage in research related to the exploration and recognition of oil and natural gas deposits
Several quality-assurance steps were performed on well logs before the statistical analysis:
Checking data continuity and the uniformity of the sampling step for all logs; applying the required environmental corrections before identifying and processing the electrofacies; combining logs from measurements from other intervals and depth shifting; depth matching between core and well logs; it was carried out based on the correlation between core GR measurement and log-derived one; detecting and removing the outliers and artificial anomaly; normalizing variables
Summary
Interpretation of well logging data is an important stage in research related to the exploration and recognition of oil and natural gas deposits. The availability of statistical programs enables fast and effective interpretation of the well logging data. A lithofacies can be defined as a stratigraphic units, that can be distinguishable from the adjacent beds based on the lithological characteristics such as mineralogical, petrographic and paleontological signatures associated with the appearance, texture or composition of the rock. They were deposited in a similar environment and have a similar diagenetic history [1]
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