Carbonates exhibits diverse flow characteristics at pore scale. Petrographic study reveals micro-level heterogeneities. Thin sections are key to assess reservoir quality although these are images and interpretations in text format. Thin section microscopic analysis is descriptive and subjective. To an extent, optical point counting is routinely used quantitatively to estimate porosity, cement, and granular features. Overall, thin section descriptions require specialist human skill and an extensive effort, as it is repetitive and time consuming. Thus, a manual process limits the overall progress of rock quality assessment. There is no recognized method to handle thin sections for direct input with conventional core data due to its image and descriptive nature of data. An automated image processing is one of the emerging concepts designed in this paper to batch process thin sections for digital reservoir descriptions and cross correlating the results with conventional core analysis data. Thin section images are photomicrographs under plane polarized light. Initially, denoise and image enhancement techniques were implemented to preserve elemental boundaries. Computational algorithms mainly, multilevel thresholding and pixel intensity clustering algorithms were programmed to segment images for extracting elements from segmented regions. The extracted elements were compared with original image for labeling. The labeled elements are interpreted for geological elements such as matrix, pores, cement, and other granular content. The interpreted geological elements are then measured for their physical properties like area, equivalent diameter, perimeter, solidity, eccentricity, and entropy. 2D-Porosity, polymodal pore size distribution, mean pore size, cement and granular contents were then derived for each thin section image. The estimated properties were compared with conventional core after calibrating with laboratory NMR data. The whole process is automated in a batch process for a specific reservoir type and computational cost is analyzed for optimization. 2D-porosity is in excellent agreement with core porosity, thus reducing uncertainty that arises from visual estimations. Scale related issues were highlighted between 2D porosity and core porosity for some samples. Polymodal pore size distributions are in good correlation with NMR T2 distribution compared to MICP distributions. The correlation coefficient was understood to be equivalent to surface relaxivity. A digital dataset consisting of 2D porosity, eccentricity, entropy, mean pore size, cement and grain contents is automatically extracted in csv format. The digital dataset, which was previously in text format in conventional analysis, is now a rich quantitative dataset. This paper demonstrated a unique and customized solution to extract digital reservoir descriptions for geoscience applications. This significantly reduced the subjectivity in visual descriptions. The solution presented is scalable to large number of samples with significant reduction in turnaround and effort compared to conventional techniques. Additional merit is that the result from this method has direct correlation to conventional core data for improving rock typing workflows. This paper presents a novel means to use thin section images directly in digital format in geoscience applications.
Log permeability for reservoir models is generally sourced from core measurements by calibrating to well-test permeability. Conventional approaches are challenged in carbonate reservoirs due to complex depositional and diagenetic alterations. The calibration to well test usually has the basis of pressure buildup (PBU) analysis from limited pilot holes. Horizontal drilling enhanced reservoir recoveries by horizontal drain holes instead of vertical producers. The certainty of a robust permeability model is scrutinized in history matching at the horizontal drain holes. Therefore, it is mandatory to reconcile the log permeability to drawdown permeability in horizontal drain holes by honoring measurement path. The measurement path brings in noticeable effects to permeability in heterogeneous carbonate reservoirs if it is pressure build up or pressure drawdown. The objective of paper is to demonstrate a solution to permeability estimation by sensitizing measurement type, scale, path, and environment in horizontal wells. An analytical workflow is developed with field examples by integrating Multi-Probe production logging (PLT) with downhole gauge data while flowing. The prerequisites are to have gauge data for a sustained and stable flow period followed by a long shut in for pressure build up. Thereafter, Multi-Probe production logging was acquired for flowing passes and shut-in passes. In general, pressure transient behavior in horizontal well is mathematically represented by pressure diffusivity equation (Goode & Thambynayagam, 1987) with four possible flow periods. An automated process is programmed in python to detect transient flow regime from gauge data. This denotes the possible flow regime with a characteristic slope which represent the transient conditions during production logging. Multi-Probe PLT data is processed for inflow profile and zonal contributions from velocity and holdup profile conforming to reservoir flow units interpreted in vertical pilot well. Drawdown permeability is estimated from the solutions to pressure diffusivity equation based on estimated downhole fluid rates, identified flow regime and boundary conditions. A discrete drawdown permeability from PLT flow profile is estimated as per transient flow regime in horizontal drain holes of a heterogeneous carbonate reservoir. Reconciliation of log permeability with drawdown permeability distinguished prominent flow units in the reservoirs. The results highlighted the critical pitfalls in static to dynamic reconciliation related to reservoir heterogeneity, measurement path, apparent skin variation across flow units and multi-phase effects. The workflow had overcome averaging nature of PBU permeability and the data scarcity in terms of PBU in vertical pilot wells. The demonstrated solution involves an automated process to quickly detect flow regime and highlights the integration of prior gauge survey with production logging results. The merit of the solution is to detect baffles and to investigate performance of high permeability streaks across drain holes by reconciling log permeability with flow-calibrated drawdown permeability. The analytical workflow is pragmatic for reducing uncertainty in permeability distribution by capturing core scale heterogeneity and honoring transient production behavior.
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