Method for characterizing subterranean formation is described. One method involves injecting a fluid into an active well of the subterranean formation at a pressure sufficient to induce one or more hydraulic fractures. Measuring, via a pressure sensor, a poroelastic pressure response caused by inducing of the one or more hydraulic fractures. The pressure sensor is in at least partial hydraulic isolation with the one or more hydraulic fractures.

Systems and methods include a computer-implemented method: Total clay, effective porosity (PHIE) values, and gas volume are determined for a tight gas sandstone reservoir characterization using petrophysical evaluation results. Regions of the tight gas sandstone reservoir are characterized, including performing diagenetic rock typing for the regions using the total clay and PHIE values, where the diagenetic rock typing reflects porosity/permeability and clay content change. Sweet spots are determined based on the diagenetic rock typing and gas volume variation. Ranked sweet spots are determined by indexing and ranking the sweet spots by category. Optimized infill drilling locations and target zones are determined, and well placements in the tight gas sandstone reservoir are assisted using the diagenetic rock typing and the ranked sweet spots.

Systems, methods, and apparatuses for determining pore volume and pore volume compressibility of secondary porosity in rock samples is disclosed. In some implementations, determining a pore volume of a secondary porosity in a rock core sample may include saturating the rock sample with deuterium oxide (D2O) by applying a vacuum to the core sample covered by D2O; centrifuging the saturated rock sample at a selected rotational speed in the presence of a second fluid to displace a portion of the D2O from the rock sample with the second fluid; measuring the rock sample with low-field .sup.1H nuclear magnetic resonance (NMR) to determine a volume of the second fluid within the rock sample; and determining a pore volume associated with a secondary porosity based on the volume of the second fluid within the rock sample.

An oil well production method in which a plurality of producers are arranged in a horizontal direction, includes boring a monitor well adjacent to one of the producers in the horizontal direction, installing a measurement optical fiber cable in the monitor well, performing Brillouin measurement and Rayleigh measurement for a strain distribution, a pressure distribution, and a temperature distribution of the monitor well along the measurement optical fiber cable over a period in which a fracture occurs hydraulically in the producers and an oil producing period, analyzing data measured through the Brillouin measurement and the Rayleigh measurement, and determining an arrangement interval of the producers in the horizontal direction and a hydraulic fracturing parameter.

A method of fluid substitution, wherein an initial data set is provided, wherein a substituted data set is provided, wherein a rock physics model is provided, wherein the initial data set includes initial data of a geophysical parameter and initial fluid data, and wherein the substituted data set includes substituted fluid data. The method includes using the model and the initial data set to calculate first calculated data of the geophysical parameter, using the model and the substituted data set to calculate second calculated data of the geophysical parameter, calculating the difference between the first calculated data of the geophysical parameter and the second calculated data of the geophysical parameter, and applying the difference to the initial data of the geophysical parameter to produce substituted data of the geophysical parameter.

A method and apparatus for generating an image of a subsurface region including obtaining geophysical data/properties for the subsurface region; resampling the geophysical data/properties to generate a resampled data set; iteratively (a) inverting the resampled data set with an initial prior model to generate a new model; and (b) updating the new model based on learned information to generate an updated prior model; substituting the initial prior model in each iteration with the updated prior model from an immediately-preceding iteration; and determining an end point for the iteration. A final updated model may thereby be obtained, which may be used in managing hydrocarbons. Inversion may be based upon linear physics for the first one or more iterations, while subsequent iterations may be based upon non-linear physics.

A method including: obtaining geophysical data for a subsurface region; generating, with a computer, at least two subsurface property models of the subsurface region for at least two subsurface properties by performing an inversion that minimizes a misfit between the geophysical data and forward simulated data subject to one or more constraints, the inversion including generating updates to the at least two subsurface property models for at least two different scenarios that both fit the geophysical data with a same likelihood but have different values for model materiality, with the model materiality being posed as an equality constraint in the inversion, wherein the model materiality is a functional of model parameters that characterize hydrocarbon potential of the subsurface region; analyzing a geophysical data misfit curve or geophysical data misfit likelihood curve, over a predetermined range of values of the model materiality to identify the at least two subsurface property models that correspond to a high-side and low-side, respectively, for each of the at least two subsurface properties, with the high-side and low-side quantifying uncertainties in the subsurface properties; and prospecting for hydrocarbons in the subsurface region with the at least two models that correspond to the high-side and the low-side for each of the at least two subsurface properties.

A combining mechanism for borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is provided, said mechanism comprising: at least one mechanism for assigning interpretive values to individual processed data types; at least one mechanism for combining the interpretive value data sets; and, at least one mechanism for providing an interpretation. A method of combining borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is also provided, said method comprising: assigning interpretive values to individual processed data types; combining the interpretive value data sets; and, providing an interpretation.

The embodiments of the present disclosure disclose a method and an apparatus for determining the permeability of the reservoir. The method comprises: acquiring logging data corresponding to the two zones at least; determining the permeability of a specified zone in the two zones at least based on logging data corresponding to the specified zone, wherein the specified zone represents any one of the two zones at least; setting weight values corresponding to the at least two zones respectively; and determining the permeability of the reservoir based on the weight values and the permeability respectively corresponding to the two zones at least. The technical solutions provided by the embodiments of the present disclosure can improve the accuracy of the determined permeability of the reservoir.

Systems, processes, and computer-readable media for determining lithology porosity of a formation rock from surface drilling parameters without the use of wireline logging. Lithology porosity at different depths in existing may be determined from the wireline logs. The lithology porosity may be shaly sand, tight sand, porous gas, or porous wet. A lithology porosity machine-learning model may be trained and calibrating using the data from a structured data set having surface drilling parameters from the existing wells and lithology porosity classifications from the wells. The lithology porosity machine learning model may then be used to determine a lithology porosity classification for a new well without the use of wireline logging.