Rock classification in the Haynesville Shale based on petrophysical and elastic properties estimated from well logs

2015 ◽  
Vol 3 (1) ◽  
pp. SA65-SA75 ◽  
Author(s):  
Mehrnoosh Saneifar ◽  
Alvaro Aranibar ◽  
Zoya Heidari
Keyword(s):  
Elastic Properties ◽  
Physical Models ◽  
Well Logs ◽  
Rock Properties ◽  
Fracture Treatment ◽  
Well Log ◽  
Rock Classification ◽  
Classification Techniques ◽  
Shale Reservoirs ◽  
Organic Shale

Rock classification can enhance fracture treatment design for successful field developments in organic-shale reservoirs. The petrophysical and elastic properties of formations are important to consider when selecting the best candidate zones for fracture treatment. Rock classification techniques based on well logs can be advantageous compared to conventional ones based on cores, and they enable depth-by-depth formation characterization. We developed and evaluated three rock classification techniques in organic-shale formations that incorporate well logs and well-log-based estimates of elastic properties, petrophysical properties, mineralogy, and organic richness. The three rock classification techniques include (1) a 3D crossplot analysis of organic richness, volumetric concentrations of minerals, and rock brittleness index, (2) an unsupervised artificial neural network (ANN), built from an input of well logs, and (3) an unsupervised ANN, constructed using an input of well-log-based estimates of petrophysical, compositional, and elastic properties. A so-called self-consistent approximation rock-physics model is used to estimate elastic rock properties. This model enables assessment of the elastic properties based on the well-log-derived estimates of mineralogy and shapes of rock components, in the absence of acoustic-wave velocity logs. Finally, we apply the three proposed techniques to the Haynesville Shale for rock classification. We verify the identified rock types using thin-section images and previously identified lithofacies. We determined that well logs can be directly used for rock classification instead of petrophysical, compositional, and elastic properties obtained from well-log interpretation. Direct use of well logs, instead of well-log-derived properties, can reduce uncertainty associated with the physical models used to estimate elastic moduli and petrophysical/compositional properties. The three proposed well-log-based rock classification techniques can potentially enhance fracture treatment for production from complex organic-shale reservoirs through (1) detecting the best candidate zones for fracture treatment and (2) optimizing the number of required fracture stages.

Integration of 3D Volumetric Computed Tomography Scan Image Data with Conventional Well Logs for Detection of Petrophysical Rock Classes

2021 ◽  
pp. 1-18
Author(s):  
Andres Gonzalez ◽  
Zoya Heidari ◽  
Olivier Lopez
Keyword(s):  
Ct Scan ◽  
Well Logs ◽  
Well Log ◽  
3D Ct ◽  
Rock Classification ◽  
Formation Evaluation ◽  
Rock Fabric ◽  
3D Ct Scan ◽  
Scan Data ◽  
Image Analysis Techniques

Summary Core measurements are used for rock classification and improved formation evaluation in both cored and noncored wells. However, the acquisition of such measurements is time-consuming, delaying rock classification efforts for weeks or months after core retrieval. On the other hand, well-log-based rock classification fails to account for rapid spatial variation of rock fabric encountered in heterogeneous and anisotropic formations due to the vertical resolution of conventional well logs. Interpretation of computed tomography (CT) scan data has been identified as an attractive and high-resolution alternative for enhancing rock texture detection, classification, and formation evaluation. Acquisition of CT scan data is accomplished shortly after core retrieval, providing high-resolution data for use in petrophysical workflows in relatively short periods of time. Typically, CT scan data are used as two-dimensional (2D) cross-sectional images, which is not suitable for quantification of three-dimensional (3D) rock fabric variation, which can increase the uncertainty in rock classification using image-based rock-fabric-related features. The methods documented in this paper aim to quantify rock-fabric-related features from whole-core 3D CT scan image stacks and slabbed whole-core photos using image analysis techniques. These quantitative features are integrated with conventional well logs and routine core analysis (RCA) data for fast and accurate detection of petrophysical rock classes. The detected rock classes are then used for improved formation evaluation. To achieve the objectives, we conducted a conventional formation evaluation. Then, we developed a workflow for preprocessing of whole-core 3D CT-scan image stacks and slabbed whole-core photos. Subsequently, we used image analysis techniques and tailor-made algorithms for the extraction of image-based rock-fabric-related features. Then, we used the image-based rock-fabric-related features for image-based rock classification. We used the detected rock classes for the development of class-based rock physics models to improve permeability estimates. Finally, we compared the detected image-based rock classes against other rock classification techniques and against image-based rock classes derived using 2D CT scan images. We applied the proposed workflow to a data set from a siliciclastic sequence with rapid spatial variations in rock fabric and pore structure. We compared the results against expert-derived lithofacies, conventional rock classification techniques, and rock classes derived using 2D CT scan images. The use of whole-core 3D CT scan image-stacks-based rock-fabric-related features accurately captured changes in the rock properties within the evaluated depth interval. Image-based rock classes derived by integration of whole-core 3D CT scan image-stacks-based and slabbed whole-core photos-based rock-fabric-related features agreed with expert-derived lithofacies. Furthermore, the use of the image-based rock classes in the formation evaluation of the evaluated depth intervals improved estimates of petrophysical properties such as permeability compared to conventional formation-based permeability estimates. A unique contribution of the proposed workflow compared to the previously documented rock classification methods is the derivation of quantitative features from whole-core 3D CT scan image stacks, which are conventionally used qualitatively. Furthermore, image-based rock-fabric-related features extracted from whole-core 3D CT scan image stacks can be used as a tool for quick assessment of recovered whole core for tasks such as locating best zones for extraction of core plugs for core analysis and flagging depth intervals showing abnormal well-log responses.

scholarly journals Organic content and maturation effects on elastic properties of source rock shales in the Central North Sea

2019 ◽  
Vol 7 (2) ◽  
pp. T477-T497 ◽  
Author(s):  
Jørgen André Hansen ◽  
Nazmul Haque Mondol ◽  
Manzar Fawad
Keyword(s):  
Elastic Properties ◽  
Source Rock ◽  
North Sea ◽  
Empirical Relation ◽  
Organic Content ◽  
Rock Properties ◽  
Geochemical Data ◽  
Well Log ◽  
Seismic Properties ◽  
Central North Sea

We have investigated the effects of organic content and maturation on the elastic properties of source rock shales, mainly through integration of a well-log database from the Central North Sea and associated geochemical data. Our aim is to improve the understanding of how seismic properties change in source rock shales due to geologic variations and how these might manifest on seismic data in deeper, undrilled parts of basins in the area. The Tau and Draupne Formations (Kimmeridge shale equivalents) in immature to early mature stages exhibit variation mainly related to compaction and total organic carbon (TOC) content. We assess the link between depth, acoustic impedance (AI), and TOC in this setting, and we express it as an empirical relation for TOC prediction. In addition, where S-wave information is available, we combine two seismic properties and infer rock-physics trends for semiquantitative prediction of TOC from [Formula: see text] and AI. Furthermore, data from one reference well penetrating mature source rock in the southern Viking Graben indicate that a notable hydrocarbon effect can be observed as an addition to the inherently low kerogen-related velocity and density. Published Kimmeridge shale ultrasonic measurements from 3.85 to 4.02 km depth closely coincide with well-log measurements in the mature shale, indicating that upscaled log data are reasonably capturing variations in the actual rock properties. Amplitude variation with offset inversion attributes should in theory be interpreted successively in terms of compaction, TOC, and maturation with associated generation of hydrocarbons. Our compaction-consistent decomposition of these effects can be of aid in such interpretations.

Integrated rock classification in the Wolfcamp Shale based on reservoir quality and anisotropic stress profile estimated from well logs

2016 ◽  
Vol 4 (2) ◽  
pp. SF1-SF18 ◽  
Author(s):  
Aderonke Aderibigbe ◽  
Clotilde Chen Valdes ◽  
Zoya Heidari
Keyword(s):  
Stress Gradient ◽  
Horizontal Stress ◽  
Well Logs ◽  
Reservoir Quality ◽  
Stress Profile ◽  
Well Log ◽  
Rock Classification ◽  
Classification Schemes ◽  
Classification Technique ◽  
Minimum Horizontal Stress

Reliable rock classification is the key to identify target zones for successful hydraulic fracturing stimulation treatment in unconventional reservoirs such as organic-rich mudrocks. Such a rock classification scheme should take into account geologic attributes, petrophysical, and geomechanical properties (i.e., in situ stress gradient and elastic properties) to improve the likelihood of successful fracture treatment. However, conventional rock classification methods do not take into account stress gradients in the formation. We have developed a new rock classification technique that integrates four rock classification schemes based on the (1) geologic facies, (2) reservoir quality, (3) stress profile, and (4) completion quality. The techniques applied in these classification schemes include core description and thin section analysis, well-log-based depth-by-depth petrophysical and compositional characterization, and analysis of geomechanical measurements. Geomechanical analysis of core measurements and well logs provide a depth-by-depth assessment of minimum horizontal stress assuming vertical transverse isotropy in the formation. We have performed the geologic facies and reservoir quality classifications using an artificial neural network analysis, in which well logs and well-log-based estimates of the petrophysical and compositional properties were inputs to the network. Our technique was applied to a well located in the Wolfcamp Shale in the Delaware Basin. Based on the integrated rock classification results, we recommend the middle of the upper Wolfcamp and the bottom of the lower Wolfcamp depth intervals as the best candidates for fracture initiation and fracture containment zones, respectively. The selection of these zones was based on the reservoir quality and average minimum horizontal stress gradient calculated in these intervals. Our integrated rock classification technique can improve the planning and execution of completions design for hydraulic fracture treatments.

A predictive anisotropic rock-physics model for estimating elastic rock properties of unconventional shale reservoirs

2019 ◽  
Vol 38 (5) ◽  
pp. 358-365 ◽  
Author(s):  
Colin M. Sayers ◽  
Sagnik Dasgupta
Keyword(s):  
Accurate Determination ◽  
Rock Physics ◽  
Seismic Inversion ◽  
P Wave ◽  
Rock Properties ◽  
Anisotropic Rock ◽  
Rock Classification ◽  
Shale Reservoirs ◽  
Physics Model ◽  
Rock Physics Model

This paper presents a predictive rock-physics model for unconventional shale reservoirs based on an extended Maxwell scheme. This model accounts for intrinsic anisotropy of rock matrix and heterogeneities and shape-induced anisotropy arising because the dimensions of kerogen inclusions and pores are larger parallel to the bedding plane than perpendicular to this plane. The model relates the results of seismic amplitude variation with offset inversion, such as P- and S-impedance, to the composition of the rock and enables identification of rock classes such as calcareous, argillaceous, siliceous, and mixed shales. This allows the choice of locations with the best potential for economic production of hydrocarbons. While this can be done using well data, prestack inversion of seismic P-wave data allows identification of the best locations before the wells are drilled. The results clearly show the ambiguity in rock classification obtained using poststack inversion of P-wave seismic data and demonstrate the need for prestack seismic inversion. The model provides estimates of formation anisotropy, as required for accurate determination of P- and S-impedance, and shows that anisotropy is a function not only of clay content but also other components of the rock as well as the aspect ratio of kerogen and pores. Estimates of minimum horizontal stress based on the model demonstrate the need to identify rock class and estimate anisotropy to determine the location of any stress barriers that may inhibit hydraulic fracture growth.

Inversion-based method for estimating total organic carbon and porosity and for diagnosing mineral constituents from multiple well logs in shale-gas formations

2013 ◽  
Vol 1 (1) ◽  
pp. T113-T123 ◽  
Author(s):  
Zoya Heidari ◽  
Carlos Torres-Verdín
Keyword(s):  
Shale Gas ◽  
Evaluation Method ◽  
Water Saturation ◽  
Total Porosity ◽  
Well Logs ◽  
Well Log ◽  
X Ray Diffraction ◽  
X Ray ◽  
Compositional Properties ◽  
Organic Shale

Reliable estimates of petrophysical and compositional properties of organic shale are critical for detecting perforation zones or candidates for hydro-fracturing jobs. Current methods for in situ formation evaluation of organic shale largely rely on qualitative responses and empirical formulas. Even core measurements can be inconsistent and inaccurate when evaluating clay minerals and other grain constituents. We implement a recently introduced inversion-based method for organic-shale evaluation from conventional well logs. The objective is to estimate total porosity, total organic carbon (TOC), and volumetric/weight concentrations of mineral/fluid constituents. After detecting bed boundaries, the first step of the method is to perform separate inversion of individual well logs to estimate bed physical properties such as density, neutron migration length, electrical conductivity, photoelectric factor (PEF), and thorium, uranium , and potassium volumetric/weight concentrations. Next, a multilayer petrophysical model specific to organic shale is constructed with an initial guess obtained from conventional well-log interpretation or X-ray diffraction data; bed physical properties are calculated with the initial layer-by-layer values. Final estimates of organic shale petrophysical and compositional properties are obtained by progressively minimizing the difference between calculated and measured bed properties. A unique advantage of this method is the correction of shoulder-bed effects on well logs, which are prevalent in shale-gas plays. Another advantage is the explicit calculation of accurate well-log responses for specific petrophysical, mineral, fluid, and kerogen properties based on chemical formulas and volumetric concentrations of minerals/kerogen and fluid constituents. Examples are described of the successful application of the new organic-shale evaluation method in the Haynesville shale-gas formation. This formation includes complex solid compositions and thin beds where rapid depth variations of mineral/fluid constituents are commonplace. Comparison of estimates for total porosity, total water saturation, and TOC obtained with (a) commercial software for multimineral analysis, (b) our organic-shale evaluation method, and (c) core/X-ray diffraction measurements indicates a significant improvement in estimates of total porosity and water saturation yielded by our interpretation method. The estimated TOC is also in agreement with core laboratory measurements.

scholarly journals Feasibility Study on the Application of Dynamic Elastic Rock Properties from Well Log for Shale Hydrocarbon Development of Brownshale Formation in the Bengkalis Trough, Central Sumatra Basin, Indonesia.

2021 ◽  
Vol 6 (2) ◽  
pp. 81-85
Author(s):  
Ahmad Muraji Suranto ◽  
Aris Buntoro ◽  
Carolus Prasetyadi ◽  
Ricky Adi Wibowo
Keyword(s):  
Young’S Modulus ◽  
Poisson’S Ratio ◽  
Young's Modulus ◽  
Well Logs ◽  
Poisson's Ratio ◽  
Rock Properties ◽  
Well Log ◽  
Log Data ◽  
Core Data ◽  
Elastic Rock

In modeling the hydraulic fracking program for unconventional reservoir shales, information about elasticity rock properties is needed, namely Young's Modulus and Poisson's ratio as the basis for determining the formation depth interval with high brittleness. The elastic rock properties (Young's Modulus and Poisson's ratio) are a geomechanical parameters used to identify rock brittleness using core data (static data) and well log data (dynamic data). A common problem is that the core data is not available as the most reliable data, so well log data is used. The principle of measuring elastic rock properties in the rock mechanics lab is very different from measurements with well logs, where measurements in the lab are in high stresses / strains, low strain rates, and usually drained, while measurements in well logging use the principle of measured downhole by high frequency sonic. vibrations in conditions of very low stresses / strains, High strain rate, and Always undrained. For this reason, it is necessary to convert dynamic to static elastic rock properties (Poisson's ratio and Young's modulus) using empirical equations. The conversion of elastic rock properties (well logs) from dynamic to static using the empirical calculation method shows a significant shift in the value of Young's Modulus and Poisson's ratio, namely a shift from the ductile zone dominance to the dominant brittle zone. The conversion results were validated with the rock mechanical test results from the analog outcrop cores (static) showing that the results were sufficiently correlated based on the distribution range.

Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data

2021 ◽  
pp. 1-50
Author(s):  
Yongchae Cho
Keyword(s):  
Seismic Data ◽  
Stochastic Approach ◽  
Fracture Network ◽  
Natural Fracture ◽  
Well Logs ◽  
Fracture Model ◽  
Well Log ◽  
Discrete Fracture ◽  
Discrete Fracture Network Modeling ◽  
Fracture Network Modeling

The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Since natural fractures are highly heterogeneous and sub-seismic scale, integrating petrophysical data (i.e., cores, well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I introduce an integrated and stochastic approach for discrete fracture network modeling with field data demonstration. In the proposed method, I first perform a seismic attribute analysis to highlight the discontinuity in the seismic data. Then, I extrapolate the well log data which includes localized but high-confidence information. By using the fracture intensity model including both seismic and well logs, I build the final natural fracture model which can be used as a background model for the subsequent geomechanical analysis such as simulation of hydraulic fractures propagation. As a result, the proposed workflow combining multiscale data in a stochastic approach constructs a reliable natural fracture model. I validate the constructed fracture distribution by its good agreement with the well log data.

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