Target-oriented time-lapse waveform inversion using deep learning assisted regularization

Detection of the property changes in the reservoir during injection and production is important. However, the detection process is very challenging using surface seismic surveys because these property changes often induce subtle changes in the seismic signals. The quantitative evaluation of the subsurface property obtained by full waveform inversion (FWI) allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational cost. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data. We apply a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small zone of interest. The redatuming technique generates time-lapse virtual data for the target-oriented inversion. Considering the injection and production wells are often present in the target zone, we can incorporate the well velocity information to the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network (DNN) to learn the statistical relationship between the inverted model and the facies interpreted from well logs. The trained network is employed to map the property changes extracted from the wells to the target inversion domain. We then perform another time-lapse inversion, in which we fit the predicted data difference to the redatumed one from observation, as well as fit the model to the predicted velocity changes. The numerical results demonstrate that the proposed method is capable of inverting for the time-lapse property changes effectively in the target zone by incorporating the learned model information from well logs.

Location-based parameterization of controlled-source electromagnetic data inversion for the monitoring of hydraulic fracturing with magnetically enhanced proppants

The injection of fluids with electromagnetic (EM) contrast is regarded as a promising tool for EM monitoring. We have developed a novel model-based inversion method, designated as a particle mapping (PM) method, as a fit-for-purpose inversion tool for EM monitoring. The PM method optimizes the location of particles while constraining a priori information on the net physical property-volume products in the inversion domain. In addition, as a regularization method for location-based inversion, a fuzzy clustering method is adopted to integrate the PM method with the geometries of an a priori anomalous distribution. The EM monitoring of hydraulic fracturing is considered as a primary application of the PM method. In particular, numerical experiments focus on the injection of magnetically enhanced proppants and the use of the fracture model as a cluster geometry. Numerical experiments also include situational assumptions of incorrect a priori injected amounts of fluids and the distribution of anomalous regions to fully investigate the practicality of the method. The results indicate not only how clear and intrinsically interpretable the imaging results can be obtained with the PM method but also how known or assumed information on injected fluids and the fracture model can be integrated with the EM inversion.

Multiscale uncertainty assessment in geostatistical seismic inversion

Geostatistical seismic inversion is commonly used to infer the spatial distribution of the subsurface petroelastic properties by perturbing the model parameter space through iterative stochastic sequential simulations/co-simulations. The spatial uncertainty of the inferred petroelastic properties is represented with the updated a posteriori variance from an ensemble of the simulated realizations. Within this setting, petroelastic realizations are generated assuming stationary and known large-scale geologic parameters (metaparameters), such as the spatial correlation model and the global a priori distribution of the properties of interest, for the entire inversion domain. This assumption leads to underestimation of the uncertainty associated with the inverted models. We have developed a practical framework to quantify uncertainty of the large-scale geologic parameters in geostatistical seismic inversion. The framework couples geostatistical seismic inversion with a stochastic adaptive sampling and Bayesian inference of the metaparameters to provide a more accurate and realistic prediction of uncertainty not restricted by heavy assumptions on large-scale geologic parameters. The proposed framework is illustrated with synthetic and real case studies. The results indicate the ability to retrieve more reliable acoustic impedance models with a more adequate uncertainty spread when compared with conventional geostatistical seismic inversion techniques. The proposed approach accounts for geologic uncertainty at the large scale (metaparameters) and the local scale (trace-by-trace inversion).

Zintl-phase Eu2ZnSb2: A promising thermoelectric material with ultralow thermal conductivity

Zintl compounds are considered to be potential thermoelectric materials due to their “phonon glass electron crystal” (PGEC) structure. A promising Zintl-phase thermoelectric material, 2-1-2–type Eu2ZnSb2 (P63/mmc), was prepared and investigated. The extremely low lattice thermal conductivity is attributed to the external Eu atomic layers inserted in the [Zn2Sb2]2- network in the structure of 1-2-2–type EuZn2Sb2(P3¯m1), as well as the abundant inversion domain boundary. By regulating the Zn deficiency, the electrical properties are significantly enhanced, and the maximum ZT value reaches ∼1.0 at 823 K for Eu2Zn0.98Sb2. Our discovery provides a class of Zintl thermoelectric materials applicable in the medium-temperature range.