Disclosed is a method for detecting a fluid, including at least one step of: measuring by at least one sensor of a wave propagating in an environment of the wave, in order to obtain at least one measured signal, the wave being particularly a mechanical wave; splitting the measured signal over a plurality of split time intervals with a predefined duration in order to obtain samples of the measured signal; computing the temporal coherence of the samples; and determination of the presence of the fluid using the computed temporal coherence.

A method is described for inverting seismic data including obtaining well logs representative of subsurface volumes of interest; generating an amplitude variation with angle (AVA) database from the well logs by seismic modeling, wherein the seismic modeling is performed a plurality of times for all combinations of fluid substitutions of brine, oil, and gas and low porosity, mid-porosity, and high porosity; generating a trained AVA model using the AVA database; obtaining a seismic dataset; calibrating the seismic dataset; computing seismic attributes for the calibrated seismic dataset using statistics for AVA classification; and generating direct hydrocarbon indicators as a function of position in the subsurface volume of interest by applying the trained AVA model to the seismic attributes. The method is executed by a computer system.

A method, including: performing, with a computer, within-seismic-attribute clustering for each of a plurality of seismic attribute datasets for N different attributes, N being greater than or equal to two; identifying an anchor attribute and N−1 subordinate attributes from the N different attributes; linking, with a computer, objects within the seismic attribute data sets corresponding to the N−1 subordinate attributes to related objects within the seismic attribute data set corresponding to the anchor attribute; and identifying, with a computer, cross-attribute clusters, wherein the objects of any subordinate attribute that are linked to a same object of the anchor attribute are part of a single cross-attribute cluster.

Methods may include normalizing two or more wellbore logs obtained from the output of two or more wellbore tool surveys of a wellbore in a formation of interest; inputting two or more wellbore logs into a correlation matrix; assigning each of the two or more wellbore logs a positive or negative value based on the impact on a selected wellbore quality; performing a principal component analysis of the two or more wellbore logs to obtain one or more loading vectors; computing weighting factors for each of the two or more wellbore logs from the one or more loading vectors; and generating a quality index by linearly combining the two or more wellbore logs using the computed weighting factors.

In some implementations, airborne electromagnetic (AEM) data and seismic data for a geographic region including sand dunes are received, and the AEM data identifies apparent resistivity as a function of depth within the sand dunes. An inversion with cross-domain regularization is calculated of the AEM data and the seismic data to generate a velocity-depth model, and the velocity depth model identifies velocity variations within the sand dunes. A seismic image using the velocity-depth model is generated.

Implementations for monitoring seismic data recorded in a marine survey of a subterranean formation for coverage gaps are described herein. Implementations include computing Fresnel sum operators for Fresnel zones of the subterranean formation based on a Kirchhoff migration impulse response at horizons of a representative plane layer model of a survey area of the subterranean formation. Implementations also include computing an acceptability map of the survey area based on the Fresnel sum operators. The acceptability map reveals coverage gaps in the survey area. Geoscientist may use the acceptability map to infill seismic data in areas of the survey area that correspond to the coverage gaps.

Method, apparatus, and computer program product are provided for retrieving analogues using topological knowledge representation (TKR). In some embodiments, a TKR input query is built and/or validated using a domain-specific knowledge base (KB). A search database containing candidate analogues and corresponding pre-built TKRs is then searched to retrieve at least one analogue of the TKR input query using statistical analysis. In some embodiments, a system may build the TKR input query based on a seismic dataset. For example, the system may receive a seismic dataset, segment the seismic dataset and classify each region using a computer vision (CV) database and the KB, and build the TKR input query based on the segmented and classified seismic dataset. In some embodiments, the TKR input query may be input and/or edited by a user. For example, the TKR input query may be input and/or edited by the user and validated using the KB.

Techniques to match a signature in seismic data with a seismic attribute space. A method includes automatically selecting a first plurality of seismic attributes corresponding to seismic data as first selected seismic attributes, combining the first selected seismic attributes into a first realization of attributes, performing a first cluster analysis on the first realization of attributes to generate a first clustered volume, selecting a region of interest (ROI) in the seismic data, projecting the ROI onto the first clustered volume to generate a first signature, determining a first level of correlation between the ROI and the first signature, and determining whether the first level of correlation between the ROI and the first signature exceeds a predetermined threshold and outputting a first correlation volume corresponding to the first signature when the first level of correlation between the ROI and the first signature exceeds the predetermined threshold.

A method of evaluating processing imprint on seismic signals includes receiving a first and a second seismic dataset of a reservoir. A first and a second synthetic dataset are generated, where the second synthetic dataset is generated by multiplying at least a portion of data in the first synthetic dataset by a scaling factor. A first and a second combined dataset are generated by adding the respective seismic dataset and the respective synthetic dataset. A first and a second processed dataset are generated by applying a seismic processing step on the first and the second combined dataset, respectively. A difference factor between the first and the second processed dataset is calculated. Based on the difference factor and the scaling factor, it is determined whether the seismic processing step is able to preserve signal amplitude changes between the first and the second seismic dataset.

A method is described for calibrating seismic data based on statistical properties of shale derived from either well logs in a volume of interest or a global database of statistical properties of shale. The method may be executed by a computer system.