A method for identifying a reef-shoal reservoir in a faulted lacustrine basin based on a basement structure-paleogeomorphology-seismic facies progressive constraint, including: analyzing a basement structure of a work area; establishing a paleogeomorphology classification standard according to thickness, reflection structure and stratigraphic dip; based on well-seismic calibration and forward modeling, establishing a seismic facies classification standard for reef-shoal facies belts under different paleo-geomorphic conditions, and quantitatively predicting and describing a reservoir in the reef-shoal facies belts using seismic facies-controlled inversion; and according to analysis results of basement structure characteristic, paleogeomorphology classification and seismic facies, establishing a method for predicting a favorable reservoir.

Methods including determining a measurement plan, having acoustic measurements, and lowering in a borehole penetrating a subsurface formation a toolstring having phased array modules. Each phased array module includes acoustic transducers operable to emit an acoustic excitation signal and receive an echo signal, as well as a programmable circuit for setting one or more variables of the phased array module. The phased array modules are configured, including programming the programmable circuit to set variables of the phased array modules according to the measurement plan. The acoustic measurements of the measurement plan are performed using the configured phased array modules. One or more of the formation, a casing disposed in the borehole, and/or an annulus between the casing and the formation are characterized using results of the performed acoustic measurements.

A method is described for seismic depth uncertainty analysis including receiving wavelet basis functions and cutoff thresholds and randomly perturbing wavelet coefficients in reduced wavelet space based on the wavelet basis functions and the cutoff thresholds to generate a plurality of random wavelet fields; receiving a reference model in a depth domain; transforming the plurality of random wavelet fields to the depth domain and combining them with the reference model to form candidate models; performing a hierarchical Bayesian modeling with Markov Chain Monte Carlo (MCMC) sampling methods using the candidate models as input to generate a plurality of realizations; and computing statistics of the plurality of realizations to estimate depth uncertainty. The method may be executed by a computer system.

The present disclosure provides a diffracted wave imaging method, device and electronic apparatus. The method comprises: acquiring pre-stack seismic wave field data of a to-be-processed area; extracting target data of a target imaging point; fitting target time sample points in the target data based on the Gaussian model and solving the fitting function to determine a distribution range of the stationary point position signal of the reflected wave in the target data; determining migration imaging data of the target imaging point based on the target data and the distribution range; and determining a diffracted wave imaging result of the to-be-processed area based on the migration imaging data of all the imaging points in the to-be-processed area.

A method and system to be used in well inspection. An acoustic signal is transmitted from a well inspection tool into a well structure and one or more return signals is detected using at least one receiver. At least one processor is used to generate variable density log (VDL) data that includes multiple waveforms in a time domain from the one or more return signals. A number of independent components to be used based on variances in the VDL data is determined and the multiple waveforms are decomposed into multiple components associated with one or more local structure variances of the well structure using independent component analysis (ICA) and the number of independent components. Characteristics of the well structure is determined based in part on patterns or features associated with one or more independent components from the multiple components.

A method is described for data analytics including receiving a dataset representative of a subsurface volume of interest; identifying at least two features in the dataset; performing optimization methods to fit the at least two features to a response variable; calculating partial dependency functions of the at least two features; calculating a simplicity of each of the partial dependency functions; calculating an importance of each of the at least two features; selecting at least one highly ranked feature based on a combination of the simplicity and the importance; and performing optimization methods to fit the at least one highly ranked feature to a response variable. The method may be executed by a computer system.

A method is described for localized seismic imaging around a wellbore. The method uses at least one non-conventional seismic source such as an electrical submersible pump to generate seismic signals which are reflected by the surrounding wellbore and rock formation and recorded by a fiber optic cable or downhole geophones. The seismic data is then processed and imaged to generate an image of the volume around the wellbore. The method may be executed by a computer system.

A method is provided of inspecting a nested multi-layer structure including a first and second electrically conductive layer and a third layer disposed behind the second conductive layer. The method includes deploying a sensor device including an electromagnetic acoustic transducer to a borehole location proximate to the structure, generating a drive signal including a plurality of frequencies, applying an electrical current signal to the sensor device based on the drive signal and inducing currents in the first conductive layer that induce currents generating acoustic signals having the plurality of frequencies, detecting a first set of resonant frequencies based on received electromagnetic signals, detecting a second set of resonant frequencies based on received acoustic signals, estimating a property of the first and/or the second conductive layer based on the first set of resonant frequencies, and estimating a property of the third layer based on the second set of resonant frequencies.

A method can include receiving a first trained machine model trained via unsupervised learning using unlabeled seismic image data; receiving labeled seismic image data acquired via an interactive interpretation process; and building a second trained machine model, as initialized from the first trained machine model, via supervised learning using the received labels, where the second trained machine model predicts stratigraphy of a geologic region from seismic image data of the geologic region.

A method may include determining an energy factor based on scratch test data and ultrasonic wave data regarding a geological region of interest. The method may further include determining an amount of inelastic energy regarding the geological region of interest using triaxial compression data and rock property data. The method may further include determining a tensile strength regarding the geological region of interest using Brazilian test data. The method may further include generating a geomechanical model regarding the geological region of interest using the energy factor and the amount of inelastic energy. The geomechanical model may include various brittleness values for the geological region of interest. The method may further include determining an injection fluid pressure to induce a hydraulic fracture at a predetermined location in the geological region of interest using the geomechanical model, the tensile strength, and fracture plane roughness data.