Modeling the elastic properties of organic shale has been of long-standing interest for source rocks and unconventional reservoir characterization. Organic shales exhibit significant variabilities in rock texture and reservoir properties at different maturity stages, subsequently affecting their elastic responses. We have developed a new rock-physics modeling scheme honoring the maturity levels (immature, mature, and overmature), which are constrained by the evolution of the physical properties of organic shale upon kerogen maturation. In particular, at different maturity stages, the manners in which the compliant organic materials interact with the inorganic mineral matrix are characterized by different effective medium theories. On the basis of the developed rock-physics templates, organic shales have different elastic behaviors at different maturity stages. Ignoring the impact of kerogen maturation is insufficient to adequately characterize the elasticity of the whole organic shale system. Modeling results suggest that the elastic responses of organic shale are sensitive to two dominant factors — organic matter content and mineralogical composition. The elastic anisotropy characteristics are not only affected by the kerogen content and clay alignment but also depend on the morphology of kerogen distribution. Our results compare satisfactorily with data from ultrasonic velocity and log measurements, confirming validity and applicability of our modeling framework.

Seismic inversion uses observed data, such as well-logging and seismic data, to infer rock parameters. However, it still suffers from nonunique solutions. The nonuniqueness increases when extrapolating away from the well location. To mitigate this effect, we incorporate geologic information into a deep-learning (DL) framework in the form of generated probabilistic labels. We transform geologic information into reservoir heterogeneity (RH) weights and geologic pattern constraint (GPC) weights to generate probabilistic labels. The RH weights are used to capture the variability in accuracy during the spatial extrapolation of well data. The RH weights are used to balance the constraint weights between well and seismic data in the DL model. The GPC weights are used to characterize the changes in geologic patterns from well locations to nonwell locations. Based on variations in local prestack seismic waveforms, the DL model learns extrapolation patterns of well data with similar geologic patterns. By combining the rock parameters derived from well logs with these two types of weights, we can generate a series of probability labels. Two types of geologic information and well-logging data are deeply integrated into a supervised 2D residual block UNet framework. In this way, we develop a novel DL-based prestack multitrace seismic inversion method. We validate our method on a 3D synthetic model and a 3D field data set. The results indicate that our method exhibits great potential for characterizing the spatial variation in subsurface rocks and outperforms traditional DL methods.

Seismic impedance inversion is one of the key techniques for quantitative seismic interpretation. Most conventional post-stack seismic impedance inversion approaches are based on the linear theory, whereas the relationship between seismic response and impedance is highly nonlinear. Thus, it is challenging to implement conventional inversion methods to obtain high-resolution impedance for reservoir investigation. Convolutional neural network (CNN), a superior deep neural network, has a strong learning ability, which can learn from data and establish complex nonlinear mapping. However, CNN-based methods are generally heavily dependent on amounts of labeled data. Hence, an alternative seismic inversion approach is proposed that combines the closed-loop CNN and geostatistics. The closed-loop CNN is less dependent on labeled data, characterized by utilizing labeled data and unlabeled data simultaneously to train the neural network. The two subnets represent forward modeling and inversion respectively, constraining each other during the neural network training. Geostatistics can be used to enrich the training data for neural network training, taking into account geological and geophysical prior information. Synthetic data testing reveals that the proposed inversion scheme can obtain more reasonable results benefiting from labeled training data augmentation. The proposed inversion scheme is applied to the field data for identifying thin interbedded reservoir within delta depositional system. The predicted results obtained by the proposed inversion scheme are consistent with well log data and geological settings, offering insights into reservoir characterization and hydrocarbon identification.

Obtaining accurate subsurface Q (quality factor) models using full-waveform inversion (FWI) methods remains a challenging task. The forward modeling problem of viscoelastic wave propagation can be solved by superimposing N rheological bodies of Maxwell or Zener type with generalized standard linear solid rheology. However, different approaches were proposed to calculate the attenuation sensitivity kernels in viscoelastic FWI. This study reviews and compares previous theories for constructing the viscoelastic sensitivity kernels. Furthermore, we derive the viscoelastic sensitivity kernels directly following the adjoint-state (or Lagrangian multiplier) method. Compared to previous approaches, we reveal that the Q sensitivity kernels can be calculated with adjoint memory strain variables. In the numerical experiments, different methods are used to calculate the viscoelastic sensitivity kernels for comparison. We have found that when simultaneously inverting for velocity and Q models, these methods can provide inversion results of comparable quality. However, in the event of inaccurate velocity structures, the Q sensitivity kernels calculated with memory strain variables can resolve the Q anomalies more clearly, while suffering from fewer parameter trade-offs.

The Ordos Basin is located in the western part of the North-China Platform, where China’s largest shale oil field was discovered. Most of the production originates from relatively thin and tight sand bodies within the Chang 7 member of the Triassic Yanchang Formation. This consequently requires accurate and high-resolution inversion data to characterize the reservoirs and define the horizontal well paths. In this paper, we adopt full-waveform inversion (FWI) to obtain the velocity model for time-to-depth inversion. Compared with conventional migration velocity analysis, FWI can produce velocity models with more high-wavenumber variations that reflect thin and localized geologic features. Geologic statistical inversion is also applied to resolve the thin beds beyond seismic resolution. A comprehensive index is then computed with the inverted parameters to evaluate the reservoir and build a sweet-spot map. We define the coordinates, direction, and length of the horizontal well on the maps. The well path is subsequently carefully designed on the sections of the time-to-depth converted inversion data. The effectiveness of the workflow is verified using the application case of the Qingcheng oil field at the Ordos Basin.