Guochang Wang

Organic-rich Marcellus Shale lithofacies modeling and distribution pattern analysis in the Appalachian Basin

The Marcellus Shale is considered to be the largest unconventional shale-gas resource in the United States. Two critical factors for unconventional shale reservoirs are the response of a unit to hydraulic fracture stimulation and gas content. The fracture attributes reflect the geomechanical properties of the rocks, which are partly related to rock mineralogy. The natural gas content of a shale reservoir rock is strongly linked to organic matter content, measured by total organic carbon (TOC). A mudstone lithofacies is a vertically and laterally continuous zone with similar mineral composition, rock geomechanical properties, and TOC content. Core, log, and seismic data were used to build a three-dimensional (3-D) mudrock lithofacies model from core to wells and, finally, to regional scale. An artificial neural network was used for lithofacies prediction. Eight petrophysical parameters derived from conventional logs were determined as critical inputs. Advanced logs, such as pulsed neutron spectroscopy, with log-determined mineral composition and TOC data were used to improve and confirm the quantitative relationship between conventional logs and lithofacies. Sequential indicator simulation performed well for 3-D modeling of Marcellus Shale lithofacies. The interplay of dilution by terrigenous detritus, organic matter productivity, and organic matter preservation and decomposition affected the distribution of Marcellus Shale lithofacies distribution, which may be attributed to water depth and the distance to shoreline. The trend of normalized average gas production rate from horizontal wells supported our approach to modeling Marcellus Shale lithofacies. The proposed 3-D modeling approach may be helpful for optimizing the design of horizontal well trajectories and hydraulic fracture stimulation strategies.

Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin

Unconventional shale reservoirs as the result of extremely low matrix permeability, higher potential gas productivity requires not only sufficient gas-in-place, but also a high concentration of brittle minerals (silica and/or carbonate) that is amenable to hydraulic fracturing. Shale lithofacies is primarily defined by mineral composition and organic matter richness, and its representation as a 3-D model has advantages in recognizing productive zones of shale-gas reservoirs, designing horizontal wells and stimulation strategy, and aiding in understanding depositional process of organic-rich shale. A challenging and key step is to effectively recognize shale lithofacies from well conventional logs, where the relationship is very complex and nonlinear. In the recognition of shale lithofacies, the application of support vector machine (SVM), which underlies statistical learning theory and structural risk minimization principle, is superior to the traditional empirical risk minimization principle employed by artificial neural network (ANN). We propose SVM classifier combined with learning algorithms, such as grid searching, genetic algorithm and particle swarm optimization, and various kernel functions the approach to identify Marcellus Shale lithofacies. Compared with ANN classifiers, the experimental results of SVM classifiers showed higher cross-validation accuracy, better stability and less computational time cost. The SVM classifier with radius basis function as kernel worked best as it is trained by particle swarm optimization. The lithofacies predicted using the SVM classifier are used to build a 3-D Marcellus Shale lithofacies model, which assists in identifying higher productive zones, especially with thermal maturity and natural fractures.

The application of improved NeuroEvolution of Augmenting Topologies neural network in Marcellus Shale lithofacies prediction

The organic-rich Marcellus Shale was deposited in a foreland basin during Middle Devonian. In terms of mineral composition and organic matter richness, we define seven mudrock lithofacies: three organic-rich lithofacies and four organic-poor lithofacies. The 3D lithofacies model is very helpful to determine geologic and engineering sweet spots, and consequently useful for designing horizontal well trajectories and stimulation strategies. The NeuroEvolution of Augmenting Topologies (NEAT) is relatively new idea in the design of neural networks, and shed light on classification (i.e., Marcellus Shale lithofacies prediction). We have successfully enhanced the capability and efficiency of NEAT in three aspects. First, we introduced two new attributes of node gene, the node location and recurrent connection (RCC), to increase the calculation efficiency. Second, we evolved the population size from an initial small value to big, instead of using the constant value, which saves time and computer memory, especially for complex learning tasks. Third, in multiclass pattern recognition problems, we combined feature selection of input variables and modular neural network to automatically select input variables and optimize network topology for each binary classifier. These improvements were tested and verified by true if an odd number of its arguments are true and false otherwise (XOR) experiments, and were powerful for classification.

A New Parameter as an Indicator of the Degree of Deformation of Coals

AbstractThe deformation of coal is effected by thermal effect, pressures and tectonic stress, and the tectonic stress is the principal influence factor. However, the proposition of a useful quantitative index that responds to the degree of deformation of coals quantitatively or semi-quantitatively has been a long-debated issue. The vitrinite reflectance ellipsoid, that is, the reflectance indication surface (RIS) ellipsoid is considered to be a strain ellipsoid that reflects the sum of the strain increment caused by stress in the process of coalification. It has been used to describe the degree of deformation of the coal, but the effect of the anisotropy on the RIS ellipsoid has not yet been considered with regards to non-structural factors. In this paper, Wei’s parameter (ε) is proposed to express the deformation degree of the strain ellipsoid based on considering the combined influence of thermal effect, pressure and tectonic stress. The equation is as follows: ε=√[(ε1-ε0)2+(ε2-ε0)2+(ε3-ε0)2]/3, where ε1=ln Rmax, ε2=ln Rint, ε3=ln Rmin, and ε0=(ε1+ε2+ε3)/3. Wei’s parameter represents the distance from the surface to the spindle of the RIS logarithm ellipsoid; thus, the degree of deformation of the strain ellipsoid is indicated quantitatively. The formula itself, meanwhile, represents the absolute value of the degree of relative deformation and is consequently suitable for any type of deformation of the strain ellipsoid. Wei’s parameter makes it possible to compare degrees of deformation among different deformation types of the strain ellipsoid. This equation has been tested in four types of coal: highly metamorphic but weakly deformed coal of the southern Qinshui Basin, highly metamorphic and strongly deformed coal from the Tianhushan coal mining area of Fujian, and medium metamorphic and weakly or strongly deformed coal from the Huaibei Coalfield. The results of Wei’s parameters are consistent with the actual deformation degrees of the coal reservoirs determined by other methods, which supports the effectiveness of this method. In addition, Wei’s parameter is an important complement to the indicators of the degrees of deformation of coals, which possess certain theoretical significance and practical values.

Evaluation on an original resistivity inversion method of water flooding a conglomerate reservoir based on petrophysical analysis

An accurate inversion of original reservoir resistivity is an important problem for waterflood development in oilfields in the middle-late development period. This paper describes the theoretical model of original resistivity recovery for a conglomerate reservoir established by petrophysical models, based on the stratigraphic model of reservoir vertical invasion of the conglomerate reservoir of an oilfield. Likewise two influencing factors of the resistivity change with a water-flooded reservoir were analyzed. The first one is the clay volume decrease due to an injected water wash argillaceous particle and the reservoir resistivity changes are influenced by it, and the other is to inject water to displace crude oil in the pore space leading to the increase of the water-bearing volume. Moreover the conductive ions of the injected water and the original formation water exchange and balance because of their salinity difference, and the reservoir resistivity changes are also influenced by them. Through the analysis of the above influential factors based on the fine identification of conglomerate lithologies the inversion models of three variables, including changes in the amount of clay, the resistivity of the irreducible water and the increase of the water bearing volume, were established by core analysis data, production performance and well logging curves information, and accurately recovered the original reservoir resistivity of the conglomerate. The original oil saturation of the reservoir was calculated according to multiple linear regression models. Finally, the produced index is defined as the difference of the original oil saturation and current oil saturation to the original oil saturation ratio, and it eliminates the effects of conglomerate lithologies and heterogeneity for the quantitative evaluation of flooded layers by the use of the principle of relative value. Compared with traditional flooding sensitive parameters which are oil saturation and water production rate, the interpretation accuracy of the production index can achieve 82%, provide technical support for the development programs determination and the well adjustment pattern in the second development of the oilfield.