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.

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.

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.

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.

Diagnostic Plots of Pressure Transient, Rate-Transient, and Diagnostic Fracture-Injection/Falloff Tests

2021 ◽  
Author(s):  
David Craig ◽  
Thomas Blasingame
Keyword(s):  
Flow Regimes ◽  
Horizontal Stress ◽  
Test Theory ◽  
Pressure Transient ◽  
Diagnostic Plots ◽  
Diagnostic Plot ◽  
New Ideas ◽  
Characteristic Features ◽  
Pressure Transient Test ◽  
Minimum Horizontal Stress

Abstract All transient test interpretation methods rely on or utilize diagnostic plots for the identification of wellbore or fracture storage distortion, flow regimes, and other parameters (e.g., minimum horizontal stress). Although all "test" interpretations of interest are transient test data (i.e., those involving an "event"), the associated diagnostic plots are not interchangeable between such tests. The objective of this work is to clearly define the appropriate diagnostic plot(s) for each type of transient test. The work applies the appropriate transient test theory to demonstrate the applicability of each diagnostic plot along with clearly defining the characteristic features that make a given plot "diagnostic." For pressure transient testing, the material is largely a review, but for rate transient tests and diagnostic fracture-injection/falloff tests, new ideas are introduced and documented to justify appropriate diagnostic plots. Data examples are provided for illustration and application. In general, pressure transient test diagnostic plots are not misused, but the same cannot be said for diagnostic fracture-injection/falloff tests (or DFITs) where it is common to ascribe flow regimes and/or draw other erroneous conclusions based on observations from an inappropriately constructed or interpretated diagnostic plot. The examples provided illustrate both the correct diagnostic plot and interpretations, but also illustrate how data can be easily misinterpreted in common practice.

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