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<jats:p> Accurate knowledge of fracture extents generated in multistage unconventional completions remains elusive. Crosswell low-frequency distributed acoustic sensing (LF-DAS) measurements can determine the time and location of a frac hit. Knowing where and when a frac hit occurs constrains the fracture extent but does not estimate it quantitatively. A recent study on crosswell LF-DAS demonstrated a simple method to rapidly determine the instantaneous fracture propagation rate when a frac hit occurs. This method, the zero strain-rate location method (ZSRLM), is based on laboratory experiments and numerical modeling assuming a radial fracture geometry. The method estimates a fracture propagation velocity that is used to extrapolate the final fracture extent. The propagation rate is calculated based on dynamic estimates of the nearest distance from the fiber to the front of a propagating fracture. The ZSRLM is adapted to estimate the distance to the fracture front based on rectangular fracture geometries. A 3D displacement discontinuity method program generates crosswell LF-DAS strain-rate waterfall plots considering a single, rectangular fracture of constant height. Over 150 different simulations were conducted varying formation mechanical properties, fracture height, and the vertical and horizontal offset between the treatment and monitor well. For each simulated case, the ZSRLM is used to estimate the distance to the fracture front based on the simulated waterfall plots. The difference between the estimated and actual distance to the front is corrected by a shape factor. The relationship among the shape factor, fracture height ratio, and vertical offset ratio is determined. Using a shape factor improves the performance of the ZSRLM by up to a factor of two for rectangular fractures. The updated ZSRLM is applied to extrapolate final fracture extents in two field cases: a single cluster stage in the Montney Formation and a multicluster stage of an Austin Chalk completion. </jats:p>

<jats:p> Compared with conventional geophone data, distributed fiber-optic sensing, including distributed acoustic sensing (DAS), can provide better spatial coverage for imaging the subsurface with finer spatial sampling. Because DAS measures subsurface seismic responses differently than the geophone, imaging technologies (e.g., reverse time migration and full-waveform inversion) that are developed for conventional geophone data cannot be readily applied to original DAS data without causing uncertainties in phase or depth, especially when one compares the DAS imaging results against the usual geophone imaging results. Based on vertical seismic profile field data from a CO<jats:sub>2</jats:sub> sequestration site, we have compared the imaging results of the CO<jats:sub>2</jats:sub> storage reservoir associated with the DAS and the geophone data, respectively, and we illustrate the differences between the imaging results of the DAS and geophone data. The difference between the DAS and geophone imaging results could be critical in obtaining time-lapse signals for monitoring reservoir changes, e.g., in subsurface CO<jats:sub>2</jats:sub> sequestration. We propose to convert DAS to geophone data so that we can reduce the discrepancies between DAS and geophone imaging results and we therefore can reuse existing seismic imaging technologies. Two conversion methods, one physics-based and one deep-learning (DL)-based, are used for the DAS-to-geophone transformation. Field data results demonstrate that the DL-based approach can better successfully improve the alignment between the DAS and geophone images, whereas the physics-based solution is constrained by its assumption. </jats:p>

<jats:p> With the development of 5D (3D + offset + azimuth) seismic technology, the stable acquisition of anisotropy information from wide-azimuth seismic data has become a key scientific problem in the seismic inversion of fractured reservoirs. The amplitude variation with incidence and azimuth (AVAZ) inversion method using wide-azimuth seismic data is an effective way to predict the anisotropic information of the subsurface medium. However, the conventional AVAZ inversion method suffers from too many parameters to be estimated, large variation in contribution, and inversion instability. Therefore, an AVAZ inversion method with coherence-attribute constraints is developed to solve the problem of unstable inversion of anisotropic parameters. First, we use seismic coherence attributes to build a fracture-probability-distribution model containing anisotropic information of the subsurface medium, which can be used to simulate large-scale subsurface fractures and faults. Then, it is added to the objective function as an anisotropic information constraint to improve the reliability and stability of the anisotropic inversion. Furthermore, an AVAZ inversion method in a Bayesian framework is implemented by using wide-azimuth seismic data. Gaussian distribution and a smoothing background model are added to the objective function to improve the reasonableness and stability of the inversion. In addition, we develop a stepwise optimization inversion method for isotropic and anisotropic parameters, prioritizing the inversion of parameters that contribute significantly to the reflection coefficient, and then using the results of the previous inversion as the initial values for the next inversion step to achieve multiparameter inversion. This method can reduce the number of the estimated parameters and thus improve the stability of the inversion of the anisotropic parameters. Field data examples indicate that this method produces suitable inversion results even at moderate levels of noise. Therefore, we can conclude that the proposed method has good applicability and stability in predicting the anisotropy parameters of fractured shale reservoirs. </jats:p>

<jats:p> Hydraulic fracturing-induced microseismicity has drawn public attention in recent years. However, understanding the behavior of hydraulic fracture is limited due to the complex relationship between microseismicity and various geologic conditions. To further understand this question, we conduct a study to detect and locate hydraulic fracturing-induced microseismicity at a shale gas production site in Tongren, Guizhou, China. We investigate the relationship between their distribution and two important geologic factors: the brittleness index (BI) of rocks and the distribution of natural fractures. With the aid of a 3D active seismic survey, we first calculate the BI of rocks in the hydraulic fracturing region using Young’s modulus and Poisson’s ratio, compared with the locating result of fracturing-induced microseismicity, which indicates that most of the events are distributed in the area with higher BI. We then delineate natural fractures using the ant-tracking method of the 3D seismic attribute. The microseismic location is consistent with the region of natural fractures. Based on our findings, we suggest that the spatial distribution of induced microseismicity is highly controlled by the brittleness of rocks and the distribution of natural fractures in this region. This research provides insights into the factors controlling hydraulic fracturing-induced microseismicity and enhances our understanding of the complex interplay between geologic conditions and the behavior of hydraulic fractures. </jats:p>

<jats:p> Solvent extracts obtained from center-cut horizontal core plugs selected in the Upper Wolfcamp (UW) and Eagle Ford source-rock (SR) beds contain unaltered volatile (i.e., gasoline range) hydrocarbon (HC) compounds because they are extracted in a closed vial. Therefore, a C<jats:sub>7</jats:sub> source parameter, a C<jats:sub>7</jats:sub> maturity parameter, and pristane/phytane ratios are used to compare the source and thermal maturity of these petroleum and oil samples produced from nearby wells landed in the same SR reservoirs. Five distinct pay zones previously identified in the UW SR reservoir using geologic criteria each contain slightly different kinds of petroleum generated at different levels of thermal maturity. A thick overlying carbonate reservoir contains the kind of petroleum generated by the kerogen present in one underlying SR pay zone. The same source and maturity parameters demonstrate that the oil-prone kerogen present in the Eagle Ford SR beds in core plugs selected from wells located ≈7.5 mi (12 km) apart on the San Marcos Arch in South Texas formed in different depositional environments. It is difficult to allocate commingled oil samples using only core-plug extracts because solvents extract the producible oil plus a component that does not readily flow from SR reservoirs because it is sorbed in kerogen and/or on clay minerals. However, because only saturate HC compounds are used to determine the C<jats:sub>7</jats:sub> source and maturity parameters, they provide valuable insights about the nature of the free oil present in SR reservoirs and commingled oil samples. </jats:p>

<jats:p> The coal component content is an important parameter during the coal resources exploration and exploitation. Previous logging curve regression and single neural network methods have the disadvantages of low accuracy and weak generalization ability in calculating coal component content. In this study, a gray relational analysis-hybrid neural network (GRA-HNN) method is developed by combining GRA and HNN to predict coal component content in logging data. First, the correlation degree between different conventional logging data and coal components is calculated using the GRA method, and logging curves with a correlation degree of ≥0.7 are selected as the input training data set. Then, a back propagation neural network, support vector machine neural network, and radial basis function neural network of different coal components are constructed based on the selected optimal input logging data, and the weighted average strategy is used to form an HNN prediction model. Finally, the GRA-HNN method is used to predict the coal component content of coalbed methane production wells in the Panji mining area. The application results indicate that the coal component content predicted by the GRA-HNN method has the highest accuracy compared with the logging curve regression method and its single neural network model, with a maximum average relative error of 13.4%. In addition, the accuracy of coal component content predicted by some single intelligent models is not always higher than the logging curve regression method, indicating that the neural network model is not necessarily suitable for all coal component content predictions. Our GRA-HNN method not only optimizes the prediction performance of a single neural network model by selecting effective input parameters but also comprehensively considers the prediction effect of several neural network models, which strengthens the generalization ability of neural network model and increases the log interpretation accuracy of coal component content. </jats:p>

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<jats:p> The energy transition drives the energy sector to renewable energy and electrification, being the critical minerals key players in the industrial development map. They comprise rare earth elements and 35 other elements including lithium that holds the 60% of its world reserves in the so-called lithium triangle located in Argentina-Bolivia-Chile.The low electrical resistivities, variations in salt concentrations, low acoustic impedances and dynamics of the hydrogeological system, makes brine monitoring a complex geophysical exploratory problem. So, the objective is to find a suitable combination of geophysical techniques that fit the lithium exploration objectives, which are the characterization of the salt flat in depth, fluid detection, basement delineation, definition of the main structures and main faults and detection of semi-fresh water aquifers that contribute to its recharge and that are key to the water balance of the endorheic basin, which has the resource in solution. For this purpose, the evaluation of several prospecting methods in different salt flats was executed, concluding that full tensor magnetotellurics, electrical resistivity tomography and gravity comprises a toolkit that fit the objectives set.#xD;The methodology is validated in Pozuelos salt flat. The results show that the fresh water-brines contact and the recharge system were well defined and understood with the electrical resistivity tomography survey. The full tensor megnetotellurics detects two ultra-conductive units: the shallower one, interpreted as a multilayer system saturated with brines, has 400 m thickness, whereas the deeper one has a 500 thickness. Both magnetotellurics and gravity characterizes the basement and gravity successfully delineated the main structures. The geophysical interpretation is in concordance with shallow and deep exploration wells. Finally, the integration of geophysical and well data allowed the construction of a 3D static reservoir model that finds the deepest basement area at approximately 900 meters depth and discriminates eight lithofacies. </jats:p>