Conventional P-P seismic images of geothermal reservoirs are often of poor quality because P-P data tend to have a low signal-to-noise ratio across geothermal prospects. Fracture identification, fluid prediction, and imaging inside subsurface areas influenced by superheated fluids are some of the challenges facing the geothermal industry. We showed that multicomponent seismic technology is effective for addressing all of these challenges across geothermal reservoirs, even when P-P data are of low quality. Although multicomponent seismic technology has advantages in geothermal exploration, there are not many published examples of multicomponent seismic data being used to characterize geothermal reservoirs. We evaluated data examples that illustrate advantages of multicomponent seismic technology for imaging within and below zones having superheated fluids, estimating fracture attributes, analyzing reservoir trapping structures, differentiating lithologies, and predicting spatial distributions of pore fluids. All examples we tested are from the Wister geothermal field in Southern California. IntroductionConventional P-wave (P-P) seismic technology often does not provide information that engineers need to optimize geothermal energy production, usually because P-P data have a low signal-to-noise ratio (S/N) in numerous geothermal environments. The reasons why P-P data have low S/N values across geothermal systems vary from prospect to prospect, but common causes are complex faulting, high attenuation of P-waves in zones having concentrations of superheated fluids in rock pores, and dramatic lateral variations in P-wave velocities in geothermal strata.Joint interpretations of P-wave and S-wave data across oil and gas prospects provide more information about subsurface structures, lithology distributions, and pore-fluid saturants than do interpretations of only P-P data (Stewart, 2010). Fundamentally, S-wave seismic data have equal value to P-wave data in geologic interpretations, which leads to the conclusion that seismic stratigraphy analyses in any geologic province should be based on joint interpretations of P and S data rather than restricting interpretation to only single-component P-wave data (Hardage et al., 2011). Our reason for publishing this work is to provide a case history that emphasizes the importance of joint interpretations of P and S data across geothermal areas. Our study uses 3D converted-S (P-SV) data acquired at Wister geothermal
Multicomponent seismic technology has been implemented across Wister geothermal field in southern California to evaluate the potential for further development of geothermal resources. The seismic survey was positioned atop the San Andreas fault system that extends southward from the Salton Sea. An interpretation of Wister Field geology was made using both P-P and P-SV seismic data. Two formation horizons, Canebrake/ Olla/Diablo and Deguynos, were interpreted. Seismic time-structure maps were generated for each horizon. The objective of the study was to determine whether productive geothermal resources could be detected and mapped more reliably with multicomponent seismic data than with single-component P-P data. Complex faults associated with the regional San Andreas Fault system were interpreted across the 13.5 mi 2 3D image space. The structural maps created are thought to be some of the most accurate depictions of subsurface structure publicly available in this area of the Imperial Valley. Particular attention was given to documenting faults that cut across deep strata. Both P-P and P-SV seismic showed evidence of such deep faults. Rock properties were analyzed from well logs. Log data showed that clastic rocks at this site exhibited measurable differences in V P ∕V S velocity ratios for different rock types. Specifically, sand-prone intervals were associated with relatively low V P ∕V S velocity ratios, and shale-dominated intervals had higher V P ∕V S ratios. Using this rock physics behavior, V P ∕V S values derived from seismic traveltime thicknesses were useful for recognizing lithological distributions and identifying favorable reservoir facies. Seismic data across Wister Field, like seismic data across many geothermal fields, have a low signal-to-noise character. We demonstrate that a unified and integrated interpretation of P and S data, even when seismic data quality is not as good as interpreters wish, can still yield valuable information for resource exploitation.
To address the problem of fault?fracture reservoir identification, a new method based on super?resolution (SR) seismic signal reconstruction is established to identify faults, sliding fracture zones and induced fracture zones. First, based on a super?resolution generation countermeasure (SRGAN) deep learning method, an SR seismic signal reconstruction network framework is designed with a discriminant network (D), a generation network (G) and a visual geometry group network (V). Through the perceptual loss, objective control functions and iterative parameter updates, the nonlinear feature learning advantages of the deep network are introduced, the noise is eliminated, weak signals are recovered, and low resolution (LR) signals are restored, allowing the seismic signal to be reconstructed into an SR signal. Second, the SR seismic signal is used to extract the geometric attributes, such as the coherence based on the gradient structure tensor (GST) and the curvature based on the fractional derivative approximation (FDA). Third, principal component analysis (PCA) is used to reduce the feature dimension of the seismic attributes such as the GST coherence and FDA curvature and extract the principal components with the strongest correlations, thus eliminating redundant and residual noise interference, highlighting the spatial distribution and internal details of the fault?fracture reservoir, and allowing a fine description of the fault?fracture reservoir to be developed. Finally, this method achieves a good application effect for reconstructing SR seismic signals and identifying fault?fracture reservoirs in the Sichuan Basin of China.
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