Lithofacies classification in the Marcellus Shale by applying a statistical clustering algorithm to petrophysical and elastic well logs

2016 ◽  
Vol 4 (2) ◽  
pp. SE31-SE49 ◽  
Author(s):  
Kristen Schlanser ◽  
Dario Grana ◽  
Erin Campbell-Stone
Keyword(s):  
Clustering Algorithm ◽  
Marcellus Shale ◽  
Organic Content ◽  
Recognition Algorithm ◽  
Well Logs ◽  
Well Log ◽  
The Core ◽  
Petrophysical Logs ◽  
Marcellus Formation ◽  
Facies Models

Interpreting shale lithofacies is an important step in identifying productive zones in the Marcellus Shale gas play; the target reservoir can be less than 3 m (10 ft) thick with overlying shales that appear similar on certain petrophysical well logs and in the core. However, these nonreservoir-quality shales contain widely varying organic and mechanical properties. To distinguish between reservoir- and nonreservoir-quality shale facies, this classification method applies a pattern-recognition algorithm, expectation maximization, to a set of commonly available petrophysical and elastic well logs to create 1D shale facies models along the wellbore. The statistical algorithm classifies the petrophysical and elastic well-log data into defined facies using the theory that each well log contains a distribution of Gaussian curves for each facies. The method is applied to 12 wells across Pennsylvania and northern West Virginia, and it is able to discriminate between shale facies based on organic content and brittleness, characteristics which are not always evident in core. To verify the geologic accuracy of the facies models, the results are compared with the core data, well logs, mudlogs, and regional stratigraphic studies when available. In addition, we were able to designate a lithofacies-defined top to the Marcellus Formation in parts of the Appalachian Basin where the gradational contact with the overlying Mahantango Formation is poorly defined on petrophysical logs.

Evaluation of Hydraulic Fracturing Treatment with Microseismic Data Analysis in A Marcellus Shale Horizontal Well

2021 ◽  
Author(s):  
Mohamed El Sgher ◽  
Kashy Aminian ◽  
Samuel Ameri
Keyword(s):  
Reservoir Simulation ◽  
Hydraulic Fracture ◽  
Horizontal Well ◽  
Marcellus Shale ◽  
Production Performance ◽  
Well Logs ◽  
Well Log ◽  
Fracture Properties ◽  
The Core ◽  
Reservoir Simulation Model

Abstract The objective of this study was to perform an integrated analysis to gain insight for optimizing fracturing treatment and gas recovery from Marcellus shale. The analysis involved all the available data from a Marcellus Shale horizontal well which included vertical and lateral well logs, hydraulic fracture treatment design, microseismic, production logging, and production data. A commercial fracturing software was utilized to predict the hydraulic fracture properties based on the available vertical and lateral well logs data, diagnostic fracture injection test (DFIT), fracture stimulation treatment data, and microseismic recordings during the fracturing treatment. The predicted hydraulic fracture properties were then used in a reservoir simulation model developed based on the Marcellus Shale properties to predict the production performance. In this study, the rock mechanical properties were estimated from the well log data. The minimum horizontal stress, instantaneous shut-in pressure (ISIP), process zone stress (PZS), and leak-off mechanism were determined from DFIT analysis. The stress conditions were then adjusted based on the results of microseismic interpretations. Subsequently, the results of the analyses were used in the fracturing software to predict the hydraulic fracture properties. Marcellus Shale properties and the predicted hydraulic fracture properties were used to develop a reservoir simulation model. Porosity, permeability, and the adsorption characteristics were estimated from the core plugs measurements and the well log data. The image logs were utilized to estimate the distribution of natural fractures (fissures). The relation between the formation permeability and the fracture conductivity and the net stress (geomechanical factors) were obtained from the core plugs measurements and published data. The predicted production performance was then compared against production history. The analysis of core data, image logs, and DFIT confirmed the presence of natural fractures in the reservoir. The formation properties and in-situ stress conditions were found to influence the hydraulic fracturing geometry. The hydraulic fracture properties are also impacted by stress shadowing and the net stress changes. The production logging tool results could not be directly related to the hydraulic fracture properties or natural fracture distribution. The inclusion of the stress shadowing, microseismic interpretations, and geomechanical factors provided a close agreement between the predicted production performance and the actual production performance of the well under study.

scholarly journals Depositional facies model and reservoir characterization of USANI field 1, Niger delta basin, Nigeria

2017 ◽  
Vol 5 (2) ◽  
pp. 57 ◽  
Author(s):  
Godwin Aigbadon ◽  
A.U Okoro ◽  
Chuku Una ◽  
Ocheli Azuka
Keyword(s):  
Tidal Flat ◽  
Water Saturation ◽  
Well Logs ◽  
Physical Analysis ◽  
Well Log ◽  
Delta Front ◽  
Depositional Facies ◽  
Facies Model ◽  
Core Data ◽  
The Core

The 3-D depositional environment was built using seismic data. The depositional facies was used to locate channels with highly theif zones and distribution of various sedimentary facies. The integration core data and the gamma ray log trend from the wells within the studied interval with right boxcar/right bow-shape indicate muddy tidal flat to mixed tidal flat environments. The bell–shaped from the well logs with the core data indicate delta front with mouth bar, the blocky box- car trend from the well logs with the core data indicate tidal point bar with tidal channel fill. The integration of seismic to well log tie display a good tie in the wells across the field. The attribute map from velocity analysis revealed the presence of hydrocarbons in the identified sands (A, B, C, D1, D2, D4, D5). The major faults F1, F2, F3 and F4 with good sealing capacity are responsible for hydrocarbon accumulation in the field. Detailed petro physical analysis of well log data showed that the studied interval are characterized by sand-shale inter-beds. Eight reservoirs were mapped at depth intervals of 2886m to 3533m with their thicknesses ranging from 12m to 407m. Also the Analysis of the petrophysical results showed that porosity of the reservoirs range from 14% to 28 %; permeability range from 245.70 md to 454.7md; water saturation values from 21.65% to 54.50% and hydrocarbon saturation values from 45.50% to 78.50 %. The by-passed hydrocarbons were identified and estimated in low resistivity pay sands D1, D2 at depth of 2884m – 2940m, sand D5 at depth of 3114m – 3126m respectively. The model serve as a basis for establishing facies model in the field.

Assessment of Water Saturation Using Dielectric Permittivity Measurements in Formations with Complex Pore Structure: Application to the Core- and Log- Scale Domains

2021 ◽  
Author(s):  
Zulkuf Azizoglu ◽  
Zoya Heidari ◽  
Leonardo Goncalves ◽  
Lucas Abreu Blanes De Oliveira ◽  
Moacyr Silva Do Nascimento Neto
Keyword(s):  
Dielectric Permittivity ◽  
Pore Structure ◽  
Water Saturation ◽  
Rock Type ◽  
Dielectric Dispersion ◽  
Well Logs ◽  
Well Log ◽  
Dielectric Measurements ◽  
The Core ◽  
Permittivity Measurements

Abstract Broadband dielectric dispersion measurements are attractive options for assessment of water-filled pore volume, especially when quantifying salt concentration is challenging. However, conventional models for interpretation of dielectric measurements such as Complex Refractive Index Model (CRIM) and Maxwell Garnett (MG) model require oversimplifying assumptions about pore structure and distribution of constituting fluids/minerals. Therefore, dielectric-based estimates of water saturation are often not reliable in the presence of complex pore structure, rock composition, and rock fabric (i.e., spatial distribution of solid/fluid components). The objectives of this paper are (a) to propose a simple workflow for interpretation of dielectric permittivity measurements in log-scale domain, which takes the impacts of complex pore geometry and distribution of minerals into account, (b) to experimentally verify the reliability of the introduced workflow in the core-scale domain, and (c) to apply the introduced workflow for well-log-based assessment of water saturation. The dielectric permittivity model includes tortuosity-dependent parameters to honor the complexity of the pore structure and rock fabric for interpretation of broadband dielectric dispersion measurements. We estimate tortuosity-dependent parameters for each rock type from dielectric permittivity measurements conducted on core samples. To verify the reliability of dielectric-based water saturation model, we conduct experimental measurements on core plugs taken from a carbonate formation with complex pore structures. We also introduce a workflow for applying the introduced model to dielectric dispersion well logs for depth-by-depth assessment of water saturation. The tortuosity-dependent parameters in log-scale domain can be estimated either via experimental core-scale calibration, well logs in fully water-saturated zones, or pore-scale evaluation in each rock type. The first approach is adopted in this paper. We successfully applied the introduced model on core samples and well logs from a pre-salt formation in Santos Basin. In the core-scale domain, the estimated water saturation using the introduced model resulted in an average relative error of less than 11% (compared to gravimetric measurements). The introduced workflow improved water saturation estimates by 91% compared to CRIM. Results confirmed the reliability of the new dielectric model. In application to well logs, we observed significant improvements in water saturation estimates compared to cases where a conventional effective medium model (i.e., CRIM) was used. The documented results from both core-scale and well-log-scale applications of the introduced method emphasize on the importance of honoring pore structure in the interpretation of dielectric measurements.

scholarly journals Ensemble Learning for Predicting TOC from Well-Logs of the Unconventional Goldwyer Shale

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 216
Author(s):  
Partha Pratim Mandal ◽  
Reza Rezaee ◽  
Irina Emelyanova
Keyword(s):  
Ensemble Learning ◽  
Supervised Machine Learning ◽  
Well Logs ◽  
Estimation Accuracy ◽  
Canning Basin ◽  
The Core ◽  
Data Points ◽  
Shale Reservoirs ◽  
Petrophysical Logs ◽  
Rock Eval

Precise estimation of total organic carbon (TOC) is extremely important for the successful characterization of an unconventional shale reservoir. Indirect traditional continuous TOC prediction methods from well-logs fail to provide accurate TOC in complex and heterogeneous shale reservoirs. A workflow is proposed to predict a continuous TOC profile from well-logs through various ensemble learning regression models in the Goldwyer shale formation of the Canning Basin, WA. A total of 283 TOC data points from ten wells is available from the Rock-Eval analysis of the core specimen where each sample point contains three to five petrophysical logs. The core TOC varies largely, ranging from 0.16 wt % to 4.47 wt % with an average of 1.20 wt %. In addition to the conventional MLR method, four supervised machine learning methods, i.e., ANN, RF, SVM, and GB are trained, validated, and tested for continuous TOC prediction using the ensemble learning approach. To ensure robust TOC prediction, an aggregated model predictor is designed by combining the four ensemble-based models. The model achieved estimation accuracy with R2 value of 87%. Careful data preparation and feature selection, reconstruction of corrupted or missing logs, and the ensemble learning implementation and optimization have improved TOC prediction accuracy significantly compared to a single model approach.

scholarly journals Improved well logs clustering algorithm for shale gas identification and formation evaluation

2021 ◽  
Author(s):  
N. P. Szabó ◽  
B. A. Braun ◽  
M. M. G. Abdelrahman ◽  
M. Dobróka
Keyword(s):  
Cluster Analysis ◽  
Shale Gas ◽  
Clustering Algorithm ◽  
Traditional Approach ◽  
Harmful Effect ◽  
Weighted Average ◽  
Well Logs ◽  
Organic Carbon Content ◽  
Wireline Logs ◽  
Most Frequent Value

AbstractThe identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters

2013 ◽  
Vol 321-324 ◽  
pp. 1939-1942
Author(s):  
Lei Gu
Keyword(s):  
Clustering Algorithm ◽  
Experimental Results ◽  
Clustering Method ◽  
Neighborhood Graph ◽  
The Core ◽  
Random Samples

The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.

Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

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.

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.

Petrophysical Type Curves for Identifying the Electrical Character of Petroleum Reservoirs

2007 ◽  
Vol 10 (06) ◽  
pp. 711-729 ◽  
Author(s):  
Paul Francis Worthington
Keyword(s):  
Water Saturation ◽  
Early Stage ◽  
Well Logs ◽  
Log Analysis ◽  
Well Log ◽  
Formation Water ◽  
Water Conductivity ◽  
Core Data ◽  
Water Saturated ◽  
Petrophysical Evaluation

Summary A user-friendly type chart has been constructed as an aid to the evaluation of water saturation from well logs. It provides a basis for the inter-reservoir comparison of electrical character in terms of adherence to, or departures from, Archie conditions in the presence of significant shaliness and/or low formation-water salinity. Therefore, it constitutes an analog facility. The deliverables include reservoir classification to guide well-log analysis, a protocol for optimizing the acquisition of special core data in support of log analysis, and reservoir characterization in terms of an (analog) porosity exponent and saturation exponent. The type chart describes a continuum of electrical behavior for both water and hydrocarbon zones. This is important because some reservoir rocks can conform to Archie conditions in the fully water-saturated state, but show pronounced departures from Archie conditions in the partially water-saturated state. In this respect, the chart is an extension of earlier approaches that were restricted to the water zone. This extension is achieved by adopting a generalized geometric factor—the ratio of water conductivity to formation conductivity—regardless of the degree of hydrocarbon saturation. The type chart relates a normalized form of this geometric factor to formation-water conductivity, a "shale" conductivity term, and (irreducible) water saturation. The chart has been validated using core data from comprehensively studied reservoirs. A workflow details the application of the type chart to core and/or log data. The analog role of the chart is illustrated for reservoir units that show different levels of non-Archie effects. The application of the method should take rock types, scale effects, the degree of core sampling, and net reservoir criteria into account. The principal benefit is a reduced uncertainty in the choice of a procedure for the petrophysical evaluation of water saturation, especially at an early stage in the appraisal/development process, when adequate characterizing data may not be available. Introduction One of the ever-present problems in petrophysics is how to carry out a meaningful evaluation of well logs in situations where characterizing information from quality-assured core analysis is either unavailable or is insufficient to satisfactorily support the log interpretation. This problem is especially pertinent at an early stage in the life of a field, when reservoir data are relatively sparse. Data shortfalls could be mitigated if there was a means of identifying petrophysical analogs of reservoir character, so that the broader experience of the hydrocarbon industry could be utilized in constructing reservoir models and thence be brought to bear on current appraisal and development decisions. Here, a principal requirement calls for type charts of petrophysical character, on which data from different reservoirs can be plotted and compared, as a basis for aligning approaches to future data acquisition and interpretation. This need manifests itself strongly in the petrophysical evaluation of water saturation, a process that traditionally uses the electrical properties of a reservoir rock to deliver key building blocks for an integrated reservoir model. The solution to this problem calls for an analog facility through which the electrical character of a subject reservoir can be compared with others that have been more comprehensively studied. In this way, the degree of confidence in log-derived water saturation might be reinforced. At the limit, the log analyst needs a reference basis for recourse to capillary pressure data in cases where the well-log evaluation of water saturation turns out to be prohibitively uncertain.

Sign in / Sign up

Export Citation Format

Share Document