Petro-elastic facies classification in the Marcellus Shale by applying expectation maximization to measured well logs

Schlanser, Kristen; Grana, Dario; Campbell-Stone, Erin

doi:10.1190/segam2014-0939.1

Cited by 4 publications

(1 citation statement)

References 12 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is also a significant bias to use conventional well log based cutoff values to build facies models quickly. Such deterministic and simplified cutoff values may not work well in complex mudstone formations (Schlanser et al, 2014). In addition, well log cutoff values can be misleading in regional studies, while using them without normalization.…”

Section: Discussionmentioning

confidence: 99%

Shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota

Bhattacharya¹

View full text Add to dashboard Cite

2.2.2 Artificial Neural Network (ANN)……………………………………………………………………………………...45 2.2.3 Self-Organizing Map (SOM)…………………………………………………………………………………………….46 2.2.4 Multi Resolution Graph-based Clustering (MRGC)………………………………………………………….48 2.3 Bakken and Mahantango-Marcellus Shale Lithofacies……………………………………………………….49 2.4 Materials and Methods……………………………………………………………………………………………………..54 2.4.1 Pre-processing Input Petrophysical Dataset……………………………………………………………………55 2.4.2 Training and Testing the Classifiers…………………………………………………………………………………57 2.5 Results and Discussions…………………………………………………………………………………………………….58 2.6 Conclusions……………………………………………………………………………………………………………………....72 Acknowledgements…………………………………………………………………………………………………………………72 References………………………………………………………………………………………………………………………………73 CHAPTER 3…………………………………………………………………………………………………………………………………78 Integrated 3D Shale Lithofacies Modeling of the Bakken Shale in the Williston Basin, North Dakota……………………………………………………………………………………………………………………………………….78 Abstract…………………………………………………………………………………………………………………………………..79 3.1 Introduction……………………………………………………………………………………………………………………...81 3.2 Geologic Setting………………………………………………………………………………………………………………..83 3.3 Dataset and Methods………………………………………………………………………………………………………..86 3.4 Results and Discussions……………………………………………………………………………………………………..95 3.5 Suggestions for Further Study………………………………………………………………………………………….112 3.6 Conclusions……………………………………………………………………………………………………………………..112 Acknowledgements……………………………………………………………………………………………………………….114 References…………………………………………………………………………………………………………………………….114

show abstract

Section: Discussionmentioning

confidence: 99%

Shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota

Bhattacharya¹

View full text Add to dashboard Cite

show abstract

Quantitative uncertainty evaluation of seismic facies classification: A case study from northeast China

Cheng

Chen

et al. 2016

GEOPHYSICS

View full text Add to dashboard Cite

Reservoir characterization in the early stage of oilfield exploration generally has enormous uncertainty because few geophysical and well data are typically available. The uncertainty when classifying the facies with seismic data propagates throughout the processes of seismic facies classification, causing errors in the final evaluation of geologic features in an area. To quantitatively evaluate the uncertainty in seismic facies classification, we have analyzed prestack seismic data and well observations in a tight reservoir from northeast China and calculated the uncertainties throughout the process. To achieve this, the facies probabilities conditioned on different properties in each step of seismic facies classification were first derived using a probabilistic multistep inversion. Second, the associated uncertainty and maximum a posterior (MAP) of facies probabilities were evaluated by means of entropy and reconstruction rate, which assessed the degree of similarity between MAP and facies sequence within the range [0, 1]. This enabled us to investigate the influence of the uncertainty propagation on seismic facies classification. The uncertainty of the inversion results for the target reservoir was finally characterized by the calculated entropy and its indicator transform. Additionally, parameter spaces of well-log and upscaled elastic properties were restricted according to the data distribution characteristics in the crossplot. Parameter vectors that were outside the restricted scopes were excluded, reducing the computational time and uncertainty. We determined that quantitative uncertainty evaluation by entropy with a probabilistic multistep approach enabled us to explore much more details of the uncertainty propagation in the processes of seismic reservoir characterization. It should be the method of choice for risk of management and decision making in reservoir assessment.

show abstract

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

2016

View full text Add to dashboard Cite

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.

show abstract

Petro-elastic facies classification in the Marcellus Shale by applying expectation maximization to measured well logs

Cited by 4 publications

References 12 publications

Shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota

Shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota

Quantitative uncertainty evaluation of seismic facies classification: A case study from northeast China

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

Contact Info

Product

Resources

About