Schlumberger Technology Corporation

Weishu Zhao

Neil Francis Hurley

Tuanfeng Zhang

Mustafa A Al Ibrahim

Xiangdong Qiu

Steve Dyer

Methods for upscaling digital rock modeling data are described. Core-plug samples for pore-scale modeling are chosen using whole-core mini-permeability grids and conventional CT scans. Pore models or pore-network models are used for flow modeling. Borehole-scale models use MPS (Multi-Point Statistics) to combine mini-permeability grids and conventional CTscans of whole core with electrical borehole images to create 3D numerical pseudocores for each RRT (Reservoir Rock Type). Effective SCAL properties computed from various MPS borehole-scale realizations or models are used to populate interwell-scale models for each RRT. Effective properties computed from flow simulations for interwell volumes are used to populate full-field scale models. At the full-field scale, outcrop analogs, sequence stratigraphy, forward stratigraphic models, diagenetic models, and basin-scale models are combined using MPS to improve flow simulations. At every stage, REVs (representative element volumes) are computed to be certain rock heterogeneities have been captured.

Multiscale digital rock modeling for reservoir simulation

In one embodiment, the current application discloses a method comprising: performing a computed tomography (CT) porosity scan on a core sample, the core sample comprising a portion of a formation of interest; in response to the CT porosity scan, interpreting a porosity profile of the core sample; and in response to the porosity profile, modeling a response of a formation of interest to a predetermined treatment to determine a reacted formation configuration, wherein the predetermined treatment comprises an acid fluid treatment schedule, and wherein the modeling further comprises modeling acid fluid flow through the formation of interest having the porosity profile, and wherein the modeling further comprises accounting for acid reaction products during the predetermined treatment and shut-in period.

Method for quantitative prediction of matrix acidizing treatment outcomes

This subject disclosure describes methods to build and/or enhance 3D digital models of porous media by combining high- and low-resolution data to capture large and small pores in single models. High-resolution data includes laser scanning fluorescence microscopy (LSFM), nano computed tomography (CT) scans, and focused ion beam-scanning electron microscopy (FIB-SEM). Low-resolution data includes conventional CT scans, micro computed tomography scans, and synchrotron computed tomography scans.

Methods to build 3D digital models of porous media using a combination of high- and low-resolution data and multi-point statistics

This disclosed subject matter is generally related to methods for characterizing two-dimensional (2D) and three-dimensional (3D) samples to determine pore-body and pore-throat size distributions and capillary pressure curves in porous media using petrographic image analysis. Input includes high-resolution petrographic images and laboratory-derived porosity measurements. Output includes: (1) pore-body and pore-throat size distributions, and (2) simulated capillary pressure curves for both pore bodies and pore throats.

Petrographic image analysis for determining capillary pressure in porous media

Methods and systems for creating a numerical pseudocore model, comprising: a) obtaining logging data from a reservoir having depth-defined intervals of the reservoir, and processing the logging data into interpretable borehole image data having unidentified borehole image data; b) examining one of the interpretable borehole image data, other processed logging data or both to generate the unidentified borehole image data, processing the generated unidentified borehole image data into the interpretable borehole image data to generate warped fullbore image data; c) collecting one of a core from the reservoir, the logging data or both and generating a digital core data from one of the collected core, the logging data or both such that generated digital core data represents features of one or more depth-defined interval of the reservoir; and d) processing generated digital core data, interpretable borehole image data or the logging data to generate realizations of the numerical pseudocore model.

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Quincy, MA, United States of America

Weishu Zhao