The evaluation of carbonate reservoirs is a complex task because of the inherent heterogeneities that occur at all length scales. Rock properties may be defined differently at different scales and this introduces a challenge in capturing heterogeneity in a single rock volume. Heterogeneities at smaller length scales must be upscaled into larger scale volumes to better predict reservoir performance. The objective in this study is to define carbonate rock types at multiple scales and then upscale those rock types and associated properties to the whole core level. Representative core plugs were selected in a heterogeneous reservoir interval based on statistical distribution of litho-types in the core. The litho-types were described by porosity and mineralogy variations along the core length using advanced dual energy XCT imaging. Plug-scale rock types were defined on the basis of petrophysical data and geological facies. High-resolution micro to nano XCT images were integrated in the rock typing scheme. Those rock types were upscaled to the whole core level by linking the core litho-types to the plug data. The core litho-types (porosity and mineralogy) gave good representation of the whole core heterogeneity and were reliable for selecting representative samples. This allowed establishing the link between plugs and whole cores and hence upscaling rock type information to the whole core scale. The high-resolution digital images emphasized the different pore geometries in the samples and improved the definition of the rock types. Accurate porosity and permeability logs were derived along the core length and gave very good match with the plug data. Multi-scale porosity-permeability trends were investigated and found to have direct impact on the determination of upscaled permeability log at the whole core level. The paper presents an advanced and quick tool for representative sample selection and statistical core characterization in heterogeneous reservoirs. The identified rock types at multiple scales provided new insights into carbonate heterogeneity and gave upscaling options for rock types and petrophysical data. The upscaled rock types at the whole core level enhance the prediction of dynamic imbibition data along the reservoir column for improved reservoir performance.
A quantitative model of the spatial distribution of reservoir properties is key to understanding reservoir heterogeneity. Special Core Analysis (SCAL) data is essential input for static and dynamic modeling of heterogenous reservoirs. To provide highquality reliable data, the SCAL program should use the right samples from the core. Conventionally, integrated geological and petrophysical approaches are applied to select samples but they generally lack consistency and seldom incorporate upscaling options.This paper presents a novel methodology for core characterization and SCAL sample selection. SCAL data is used as input for spatial distribution of reservoir properties in a static reservoir model. The analysis is performed in siliciclastics and carbonate reservoirs from wells in the Bahrah Oil Field. An integrated X-ray, CT scanning, geological, and conventional core analysis approach is applied for understanding the reservoirs. We demonstrate the efficiency of dual-energy CT imaging in producing continuous whole core scans at 0.5 mm (500 micron) spacing and in deriving bulk density (BD) and effective atomic number (Zeff) logs along the core intervals. The high resolution 3D CT images improved the sedimentological descriptions of the core and the X-ray CT-derived numerical data (BD and Zeff) are used to derive porosity and mineralogy along the whole core sections. This information is then converted into lithology logs which predicted the cross-well correlation and enhanced the previously established correlation from conventional core descriptions. BD and Zeff cross plots suggested four lithotypes in the core intervals and the corresponding lithology log helped in deriving the percentage of each type: 1. Low BD (high pososity) carbonate formed around 20% of the whole cores. 2. High BD (low porosity) carbonate formed around 36% of the whole cores. 3. Low BD (high porosity) sandstone formed around 28% of the whole cores. 4. High BD (low porosity) sandstone formed around 16% of the whole cores.The data provided a unique capability for ensuring that the plugs adequately and correctly represented the lithotype variations along the core. The overall procedure helped minimize uncertainties in defining the rock types and effectively assign those rock types to the selected samples and core intervals.
Determination of Reservoir Rock Types (RRT) is one of the main parameters in the process of reservoir modelling and simulation. In carbonate reservoirs, the rock typing process is challenging due to multiscale heterogeneity with varying pore types and complex microstructures. The objective in this paper is to select representative samples from a heterogeneous core (350 feet) and establish unique reservoir rock types as well as model permeability along the entire core length based on textural analysis, geological interpretations and petrophysical measurements. Representative core plugs were selected in a full-diameter heterogeneous core from a carbonate reservoir in the Middle East. The sample selection was based on statistical distribution of porosity and CT-textures in the core. The porosity and textural variations were determined along the core length at 0.5 mm resolution using advanced dual energy X-ray CT imaging. Plug-scale rock types were established based on micro-textures and pore types using thin-section photomicrographs, mercury injection analysis and poroperm measurements. The micro-texture analysis (grainy, muddy, mixed) and pore types were linked to the poroperm data. The micro-texture information was then upscaled to the entire core length using CT-textural analysis. The porosity and permeability data were fitted into unique trends that were derived from the detailed textural analysis. This process provided the link between the poroperm trends and the different textures in the core enabling permeability and rock types to be upscaled to the entire whole core intervals. Variation of reservoir rock types was studied for each poro-perm trend. The different trends were mainly controlled by the different rock micro-textures whereas the extent of the trend was due to different diagenesis processes (i.e. dissolution, cementation & compaction). This paper describes a novel approach of combining textures with porosity to model permeability and rock types at the plug scale and core level. A unique dual energy CT technique was used to ensure that all the core property variations were well represented in the plug-scale core analysis measurements.
Unconventional shale reservoirs differ largely from conventional sandstone and carbonate reservoirs in their origin, geologic evolution and current occurrence. Shale is a wide variety of rocks that are composed of extremely fine-grained particles with very small porosity and permeability values in the order of few porosity units and nano-darcy range, respectively. Shale formations are very complex at the core scale, and exhibit large vertical variations in lithology and Total Organic Carbon (TOC) at a small scale that renders core characterization and sweet spot detection very challenging. Shale formations are also very complex at the nano-scale pore level where the pores have different porosity types that are detected within the kerogen volume. These complexities led to further research and development of advanced application of high resolution X-ray CT scanning on full diameter core sections to characterize shale mineralogy, porosity and rock facies so that accurate evaluation of the sweet spot locations could be made for further detailed petrophysical and petrographic studies. In this work, argillaceous shale gas cores were imaged using high resolution dual energy X-ray CT scanning. This imaging technique produces continuous whole core scans at 0.5mm spacing and derives accurate bulk density and effective atomic number (Zeff) logs along the core intervals which were crucial in determining lithology, porosity, and rock facies. Additionally, integrated XRD data and energy dispersive spectrum (EDS) analysis were acquired to confirm the mineral framework composition of the core. Smaller core plugs and subsamples representing the main variations in the core were extracted for much higher resolution X-ray CT scanning and Scanning Electron Microscopy (SEM) analysis. Porosity was mainly found in organic matter and was determined from 2D and 3D SEM images by image segmentation process. Horizontal fluid flow was only possible through the organic matter and the simulations of 3D FIB-SEM volumes by solving Stokes equation using Lattice Boltzmann Method (LBM). A clear trend was observed between porosity and permeability while correlating with identified facies in the core. Silica-rich facies gave higher Phie-K characteristics compared to the low clay-rich facies. This is mainly caused by pressure compaction effect in the soft clay-rich samples. High percentages of organic matter were not found to be good indication for high porosity or permeability in the clay-rich shale samples. The depositional facies was found to have great effect on the pore types, rock fabric and reservoir properties. The results and interpretations entailed in this study provide further insights and enhance our understanding of heterogeneity of the organic rich shale reservoir rock.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.