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1 Application of wide-azimuth 3D seismic attributes to predict the microfractures in Block MA area for shale gas exploration in South China Yusheng Zhang* 1, Gang Yu 1, Ximing Wang 1, Xing Liang 2, and Li Jiang 3, 1 BGP, CNPC, P. R. China, 2 Zhengjiang Oilfield, CNPC, P. R. China, 3 RSI, Houston, USA Summary It is the key for the success of the Paleozoic marine shale gas exploration and development in the South China to search for the sweet spots or shale gas-rich zones within the shale gas reservoir. Since it directly impacts the shale gas enrichment degree and hydro-fracturing effect of the shale gas reservoir, the development degree of shale gas reservoir fractures is the important factor for the prediction and evaluation of the sweet spots or shale gas-rich zones. This paper proposes a seismic fracture facies evaluation workflow for shale gas reservoir and its application technique through the utilization of the 100 km 2 wideazimuth 3D seismic data post stack attributes in the Block MA shale gas exploration area. We focus especially on using neural network technique to analyze the 3D seismic attribute data for fracture prediction. The examples show that using the wide-azimuth 3D seismic attribute technique could play an important role in the prediction and assessment of the gas-rich zones in shale gas reservoirs. Introduction In the exploration and production process of shale gas, the optimization of favorable shale gas areas, the prediction of sweet spots or shale gas-rich zones and the hydro-fracturing effect assessment are the three key stages. The strong laterally heterogeneous characteristics of shale gas reservoir suggest that the prediction and search for the sweet spots of shale gas reservoir are keys to the exploration and development of the shale gas field. The development degree of the fractures in the shale gas reservoir is one of the most important factors to evaluate the sweet spots or shale gas-rich zones. As has been shown by previous examples, the coherence and phase attributes of the wide-azimuth 3D seismic data can be used to effectively identify geologic phenomena such as how faults and karst column collapse. However, how to further utilize the 3D seismic attribute data to effectively predict the distribution of natural fractures still has proven to be of great difficulties. This paper discusses the application of the post stack attributes of the wide-azimuth 3D seismic data in predicting the fractures in shale gas reservoir. South China marine shale gas formation and their gasrich zones The South China marine shale gas reservoir refers to the Paleozoic marine type organic-rich shale layers dominated by a marine shelf (i.e., continental shelf) depositional environment setting and within the center of Yangtze craton basin, Having been reconstructed by many intense orogenic tectonic movements since Indosinian period (Jiang and Nie, 2008), the thermal evolution degree reached the over-mature stage and the area is dominated by the marine wide synclinal structure with good regional preservation conditions. They have been characterized having large single-layer thickness, multiple developed horizons, wide distribution area, high thermal evolution degree and strong deformation for later stage reconstructions. Regionally, the typical ejective anticlinal fold zones and fracture zones may generate dense natural microfractures more easily. The total porosity of the shale gas reservoir is less than 10% in general, but the effective porosity in the gas-rich zone is generally less than the half of the total porosity. The permeability varies greatly with the development degree of the natural microfractures. Although shale gas reservoir commonly has the feature of containing gas, the distribution of gas-rich zones is mainly dependent upon the presence of natural microfracture systems in the shale reservoir formation. This indicates that the well-developed microfracture system is an important influential factor to increase the productivity of shale gas wells. The shale gas exploration and evaluation tasks are: first, to identify the burial depth of shale reservoir formation, the thickness and the spatial spreading of high-quality shale formations, and the distribution of faults and fractures; second, to derive the engineering parameters of shale formations, such as their geomechanical properties, brittleness index, and stress field distribution. In addition, what is more essential is to understand the spreading characteristics of the natural microfractures in the exploration target layers of shale gas and their overlying and underlying strata. In 2010, a 100 km 2 wide-azimuth 3D seismic data set was acquired in the South China marine shale gas Block MA exploration area within the Sichuan Basin. Before the 3D seismic data acquisition, the drilling of Well A and the hydro-fracturing were completed in the central part of the exploration area. The exploration area belongs to the typical mountainous hilly land in South China. By performing the wide-azimuth 3D seismic data acquisition and obtaining high signal-to-noise ratio (S/N) raw seismic data, conducting prestack denoising, fidelity amplitudepreserved processing, precise velocity analysis and accurate migration imaging, several 3D seismic post-stack attributes Page 2403

2 sensible to shale formation microfracture distribution are used to make the fault and fracture prediction. These tools allow us to evaluate and predict the distribution of gas-rich zones in the shale gas reservoirs and the well locations. Post-stack 3D seismic attributes for microfracture prediction For the 3D seismic post-stack migration data, the fracture sensitive attributes are classified into 3 types, which are the coherence and correlation attribute, the dip angle and dip azimuth attribute and the curvature attribute. Each of these attributes is described individually. Coherence and correlation attribute: The first generation algorithm for coherent data volume calculation, namely, the Algorithm C 1, is composed of the coherences of the seismic data in the inline and cross-line directions; the second generation coherence method, namely, the Algorithm C 2, does not use semblance but mutual coherence to process on seismic channels, can calculate the dip angle and azimuth of the reflection surface; and the third generation coherence data volume algorithm, namely, the Algorithm C 3, is based on the principle of the eigenvector. Dip angle and dip azimuth attribute: The dip angle characteristics can be constrained to the geometric feature of formation deposit. In order to make use of the more information in interpreting sedimentary facies and structural phases, the dip angle and dip azimuth attribute plays a role of intermediately connecting the stratum continuity and the calculation of the curvature attribute. Curvature attribute: There are 6 typical curvature attributes in general, which are mean curvature, maximum curvature, minimum curvature, Gauss curvature, maximum positive curvature and maximum negative curvature respectively. These curvature attributes are expressed with 2D coefficients, namely length and delay, which describe the bending characters of one surface from different angles. The fact that faults and natural fractures can lead to the variation of seismic waves in amplitude belongs to structural attribute category; therefore, the structural attribute can be formulated with coherence, curvature and dip angle attributes to make prediction on the distribution of natural fractures. MA fracture prediction using 3D seismic structural attributes Based on the wide-azimuth 3D seismic data from the shale gas Block MA exploration area, we studied a number of structural attributes sensitive to faults and fractures, finding that the coherence attribute and some seismic attributes based on neural network analysis have an obvious effect in predicting shale gas reservoir fractures. Coherence attribute prediction: The most clear faults are in the reflection horizon at the bottom of the shale gas reservoir (T S ), reflecting the faults in three large strikes: one master fault in WE strike to the north of Well A, one visible and recognizable fault in NE-WE strike, and one small indistinct fault in N-NW to S-SW strike that connects with the large fault. Figure 1 shows the top of the shale gas reservoir formation (T S2 ) and Figure 2 shows the bottom of the shale gas reservoir formation (T S ) with coherence overlain on that horizon. The prediction effect shows that Well A is located in the central section of the exploration area with few faults and natural fractures. Figure 1: A plan view of the T S2 horizon with coherence overlain on the horizon. Figure 2: A plan view of the T S horizon with coherence overlain on the horizon. The seismic attribute prediction effect based on neural network analysis: the neural network is an adaptive statistical model that is simulated based on an analogy with the structure of the brain. Kohonen neural network selforganization mapping (KSOM) represents one kind of organization and is the most feasible method using neurons (Strecker and Uden, 2002). When starting KSOM, one set of optimistic attribute values should be input ( optimistic Page 2404

3 means that there is no correlation between the attributes and each attribute has its own independent information related to solving the target function). The KSOM method can classify the high-dimensional data volumes (multidimensional seismic attribute values) automatically in a non-linear pattern to reflect different seismic response characters. In this brainstorming study, six basic seismic attributes (coherence, maximum and minimum curvature, shape index curvature, instantaneous dip, and dip azimuth) were chosen to input into the neural net. These attributes were chosen because theoretically they are the 6 primary attributes. The other curvature attributes are derivatives of these primary attributes. KSOM was run with 100 classes (a 10x10 matrix). Since it naturally sorts the 6 input attributes into portions of this weight mapping, these maps are a critical QC of the KSOM results. The weight maps clearly show that maximum curvature response was placed in the upper center, minimum curvature response in the upper left, dip in the center, and coherence in the upper left and center (Figure 3). Within this mapping of attributes, the dip azimuth was arranged with high positive dips along the lower left of the map and high negative dips on the lower left and upper right of the map. Figure 3 shows the primary QC used for the KSOM. The boxes surround areas where anomalous responses to the 4 primary attributes (minimum and maximum curvature, coherence, dip) are located. The thin boxes surrounding 3 of these 4 anomalous areas outline specific fracture facies zones. The thick box in the coherence panel does not correspond to a specific facies zone but instead encompasses most of area contained in all three fracture-sensitive facies zones. Figure 3: KSOM weight maps (Tops maps: instantaneous dip angle, the coherence and dip azimuth from left to right; Bottom maps: maximum curvature, the minimum curvature and the curvature form index from left to right). Through analytical study on the above classifications, 5 kinds of microfracture facies are established for the evaluation of shale gas reservoir. Based on the weight map, 3 kinds of microfracture facies (color 5, 300 and 500 respectively) and 2 kinds of setting facies (color 800 and 1000) are established (Figure 4). These 5 kinds of microfracture facies reflect 5 development stages of fractures. The 5, 300 and 500 mean that the development stages of fractures go from stronger to weaker; while the setting facies 800 and 1000 mean that the fractures are undeveloped and therefore the faults and fractures development state of the shale reservoir in the Block MA are predicted and described visually. Figure 4: The results of the neural net (KSOM) fracture facies calculations in an inline view. Figure 4 shows a vertical transect through the Well A location to give an impression how the coherence maps onto the seismic and then how the fracture facies map onto the individual attributes. It is the results of neural net fracture facies calculations in an inline view. The 3 fracture-sensitive facies (5, 300, and 500) are in bright colors. Their distribution is closely tied to coherence and curvature volumes, from where the fracture facies were derived. The same impression is seen on this figure as in Figures 1 and 2 that the well was drilled within a relatively fracture-free fault block. The seismic attribute microfracture facies section provides great advantages in analyzing, checking and comparing the fracture development state. It is shown clearly that Well A is situated in the part with fewer fractures developed in the major formations of interest T S2 and T S. Figures 5 and 6 show the fracture facies overlain on the T S and T S2 horizons. T S and T S2 fracture facies planes indicate that the fault development state in Block MA is highly coincident with Figures 1 and 2. In addition, the T S fracture facies reveal clearly that fractures are extremely undeveloped around well A and the T S2 fracture facies also show that there are few fractures around Well A (Figures 5 and 6). Page 2405

4 Figure 5: The plan view of the T S horizon with the fracture facies classes overlain on that horizon. Figure 6: The plan view of the T S2 horizon with the fracture facies classes overlain on that horizon. The shale gas production rate is not ideal after multi-stage fracturing is made on the horizontal section of Well A. Analyzing in combination with the fracture prediction from the 3D seismic attributes and the microseismic monitoring result of hydro-fracturing, one of the reasons might be that the well is just in the undeveloped area of fractures with relative ductile shale formation, and therefore the gasbearing abundance of shale gas and the permeability of shale gas formations are impacted, resulting in a low constant rate of shale gas production from Well A. Based on the fracture prediction results, Well B is laid out in the exploration area where the shale gas reservoir fractures are more developed. Conclusion The 3D seismic attributes including the coherence and correlation, the dip angle, azimuth and the curvature which are sensitive to faults and fractures are analyzed comprehensively in the marine shale Block MA exploration area in South China. We use the neural network multidimensional seismic attribute clustering method to classify and identify microfracture facies, and predict the development degree of fractures and the distribution of gasrich zones in the shale gas reservoir. It is evident these facies do a very good job of identifying seismicallydetectible faults and fracture zones. Although the resolution of the fracture facies is tied directly to the resolution of the underlying seismic data, these facies volumes can be used to qualitatively determine the amount of natural fracturing that can be expected at any point within the survey area. These results support the conclusions derived from the individual attributes. They show that the Well A is located in a relatively unfractured fault block. This paper indicates that Well A, the low shale gas production rate well in the exploration area, is just in the extremely undeveloped area of fractures and the study results have guided the subsequent layout of shale gas evaluation and production wells. Acknowledgments We would like to thank CNPC for allowing the publication of this article. We would also like to thank BGP for their support and guidance during the data acquisition, processing and interpretation phases of this project. Page 2406

5 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2014 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Jiang, Y. Q., and H. K. Nie, 2008, Basic characteristics of shale gas reservoir and its evaluation: Natural Gas Industry (Chinese), 24, no. 7, 73. Strecker, U., and R. Uden, 2002, Data mining of 3D post-stack seismic attribute volumes using Kohonen self-organizing maps: The Leading Edge, 21, , Page 2407

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