This thesis introduces a rock typing method for application in shale gas reservoirs using conventional well logs and core data. Shale gas reservoirs are known to be highly heterogeneous and often require new or modified petrophysical techniques for accurate reservoir evaluation. In the past, petrophysical description of shale gas reservoirs with well logs has been focused to quantifying rock composition and organic-matter concentration. These solutions often require many assumptions and ad-hoc correlations where the interpretation becomes a core matching exercise. Scale effects on measurements are typically neglected in core matching. Rock typing in shale gas provides an alternative description by segmenting the reservoir into petrophysically-similar groups with k-means cluster analysis which can then be used for ranking and detailed analysis of depth zones favorable for production. A synthetic example illustrates the rock typing method for an idealized sequence of beds penetrated by a vertical well. Results and analysis from the synthetic example show that rock types from inverted log properties correctly identify the most organic-rich model types better than rock types detected from well logs in thin beds. Also, estimated kerogen concentration is shown to be most reliable in an under-determined problem. Field cases in the Barnett and Haynesville shale gas plays show the importance of core data for supplementing well logs and identifying correlations for desirable reservoir properties (kerogen/TOC concentration, gas saturation, and porosity). Qualitative rock classes are formed and verified using inverted estimates of kerogen concentration as a rock-quality metric. Inverted log properties identify 40% more of a high-kerogen rock type over well-log based rock types in the Barnett formation. A case in the Haynesville formation suggests the possibility of identifying depositional environments as a result of rock attributes that produce distinct groupings from k-means cluster analysis with well logs. Core data and inversion results indicate homogeneity in the Haynesville formation case. However, the distributions of rock types show a 50% occurrence between two rock types over 90 ft vertical-extent of reservoir. Rock types suggest vertical distributions that exhibit similar rock attributes with characteristic properties (porosity, organic concentration and maturity, and gas saturation). This method does not directly quantify reservoir parameters and would not serve the purpose of quantifying gas-in-place. Rock typing in shale gas with conventional well logs forms qualitative rock classes which can be used to calculate net-to-gross, validate conventional interpretation methods, perform well-to-well correlations, and establish facies distributions for integrated reservoir modeling in hydrocarbon-bearing shale. / text

The purpose of this investigation is to determine the effectiveness of projective well log analysis based upon data collected from Plummer Field located in Greene County, Indiana. Projective well log analysis consists of analyzing spontaneous potential (SP) logs from existing oil and gas wells in order to determine SP gradients that may be applied to locate other undiscovered hydrocarbon accumulations. Projective well log analysis was developed in 1963 by S.J. Pirson, however, the specific parameters employed in the Plummer Field investigation were developed in 1988 by Dr. R.H. Fluegeman in order to apply to the geologic conditions in southwestern Indiana.The results of this investigation indicate that SP gradients can be interpreted to determine hydrocarbon production potential in Plummer Field with a 62% to 73% certainty. Given the petroleum industry exploration success rate of 3% to 20%, it is believed that the SP gradients established in Plummer Field can be used to identify economical hydrocarbon accumulations in areas of similar geology such as other portions of the Illinois Basin and the Michigan Basin. / Department of Geology

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains ix, 123 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 108-109).

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 169-173). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S.)--West Virginia University, 2000. / Title from document title page. Document formatted into pages; contains ix, 111 p. : ill. (some col.), map. Includes abstract. Includes bibliographical references (p. 110-111).

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains vi, 84 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 79-82).

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains ix, 148 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 145-148).

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In this paper, we design and develop a new software tool that helps automatic lithofacies segmentation from geological data. Lithofacies is a crucial problem in reservoir characterization, and our study intends to prove that soft computing techniques like Wavelet transform modulus maxima lines (WTMM) and Detrended fluctuation analysis (DFA) approaches allow a geological lithology segmentation from differed well logging. On one hand, WTMM proves to be useful for delimitation of each layer. We based on its sensitivity on the presence of more than one layer, On the other hand, DFA is used to enhance the estimation if the roughness coefficient of each lithology. We use them jointly to segment the lithofacies of boreholes located in the Algerian Sahara. Obtained results are encouraging to publish this method, because the principal benefit is economic.

by Andrew Loris Kurkjian. / Originally published as thesis (Dept. of Electrical Engineering and Computer Science, Ph.D., 1982). / Bibliography: p. 186-189. / Supported in part by the Advanced Research Projects Agency monitored by ONR under Contract N00014-81-K-0742 NR-049-506 Supported in part by the National Science Foundation under Grant ECS80-07102