Textural analysis of FMI logs to determine lithofacies in the early paddock member
Pate, Lauren Crystal
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Abstract Reservoir characterization is defined as the process of modeling a reservoir and fully understanding aspects that impact its ability to store and produce hydrocarbons. To model a reservoir you must first know the distribution of porosity and permeability of the rock. Due to the heterogeneous of carbonates this can be very difficult. The way to understand the controls on porosity and permeability is through lithofacies analysis and understanding the affect of digenesis on the reservoir. Lithofacies analysis can be completed through core descriptions, however cores are not widely available and are costly to acquire and describe. Wireline logs can be used for lithofacies analysis, and although logs are often abundant in mature fields, the interpretations can be non-unique, vague and/or inaccurate. Image logs can be a useful tool to improve the accuracy of wireline log interpretations. The main goal of this thesis is to use FMI (Full-bore Formation Microresistivity imaging log) logs to interpret carbonate lithofacies of the Lower Permian Paddock Member of the Yeso Formation in the Loco Hills region of New Mexico. FMI (Full-bore Formation Microimager) logs are a Schlumberger image log that obtains and processes microresistivity values to from an image. Common rock types found in the Paddock are anhydrites, siltstones and dolostone, with Dunham textures ranging from grainstones to wackestones. These lithofacies represent a range of EOD’s (Environment of deposition) in the Paddock member in the Loco Hills region, including supratidal islands, subtidal and tidal flats. To do lithofacies analysis, core descriptions analysis will be was to compare and identify lithologies in the FMI logs for the test well. Textural analysis was completed on FMI logs, in order to identify lithofacies. Textural analysis is the process of examining a target pixels and determining its intensity and similarity or continuity of intensity to the surrounding pixels. To complete textural analysis the matlab image toolbox was used to identify the area and roundness of the grains in each rock type. These methods were then used to develop and producer that can be used to identify rock types. Once the textural analysis is complete and the lithofacies determined by the procedure developed, sequence stratigraphy was used to check the accuracy of the inferred lithofacies picks. The procedure developed has 82% accuracy. The lithologies that were not correctly identified usually did not affect the sequence stratigraphy. The lithologies most commonly miss identified were peloidal packstones, wackestone and packestones. Only in one occasion did incorrect pick affect the sequence stratigraphy. Although the test was a success in the well with core, it was not possible to use the same parameters for the surrounding wells. This was due to the varying permeability and porosity of the Paddock member carbonates, the procedure was not successful in the surrounding wells.