A method and apparatus for hydrocarbon management, including generating a fluid saturation model for a subsurface region. Generating such a model may include: performing a brine flood petrophysical inversion to generate inversion results; iteratively repeating: classifying rock types (including at least one artificial rock type) based on the inversion results; generating a trial fluid saturation model based on the classified rock types; performing a trial petrophysical inversion with the trial fluid saturation model to generate trial results; and updating the inversion results with the trial results; and generating the fluid saturation model for the subsurface region based on the inversion results. The petrophysical inversion may include a facies-based inversion and/or may invert for water saturation. Generating such a model may include: performing a brine flood petrophysical inversion, performing a hydrocarbon flood petrophysical inversion; identifying misfits in the inversion results, and generating a trial fluid saturation model based on the misfits.

A method of calculating the likely positions of structures in a region of the earth's crust includes defining the region in the earth's crust; creating a first structural model of the region from seismic data with uncertainties and correlations; creating a second structural model of the region from measurements in a wellbore with uncertainties and correlations; creating a third structural model of the region from measurements in a volume around the wellbore measured from the wellbore with uncertainties and correlations; defining constraining equations for the first, second and third structural models; and using said constraining equations, calculating likely positions of structures in the region, and likely uncertainties and correlations relating to the positions.

The invention is a method applicable to oil and gas exploration and exploitation for automatically detecting geological objects belonging to a given type of geological object in a seismic image, on a basis of a priori probabilities of belonging to a type of geological object assigned to each of samples of the image to be interpreted. The image is transformed into seismic attributes applied beforehand, followed by a classification method. For each of the classes, an a posteriori probability of belonging to a type of geological object is determined for each of the samples of the class according to the a priori probabilities, of the class, of belonging, and according to a parameter describing a confidence in the a priori probabilities of belonging. Based on the class of the sample, the determined a posteriori probability of belonging to a type of geological object is assigned for the samples of the class. The geological objects belonging to the type of geological object are detected based on determined of the a posteriori probabilities of belonging to the type of geological object for each of the samples of the image to be interpreted.

A method includes receiving observed seismic data, determining an envelope or magnitude of the observed seismic data as a first observed value, generating a variable noise term based in part upon the first observed value, and utilizing the variable noise term to determine a likelihood function of a stochastic inversion operation. The method also includes utilizing the likelihood function to generate a posterior probability distribution in conjunction with the stochastic inversion operation and applying the posterior probability distribution to characterize a subsurface region of Earth.

Method for source localization for cable cut prevention using distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) is described that is robust/immune to underground propagation speed uncertainty. The method estimates the location of a vibration source while considering any uncertainty of vibration propagation speed and formulates the localization as an optimization problem, and both location of the sources and the propagation speed are treated as unknown. This advantageously enables our method to adapt to variances of the velocity and produce a better generalized performance with respect to environmental changes experienced in the field. Our method operates using a DFOS system and AI techniques as an integrated solution for vibration source localization along an entire optical sensor fiber cable route and process real-time DFOS data and extract features that are related to a location of a source of vibrations that may threaten optical fiber facilities.

Systems and methods for training a model that uses probabilities of lithologies as prior information in an inversion are disclosed. Exemplary implementations may: obtain training data, the training data including (i) subsurface map data sets, and (ii) known lithologies; obtain an initial seismic mapping model; generate a conditioned seismic mapping model by training the initial seismic mapping model; store the conditioned seismic mapping model; obtain a target subsurface map data set; apply the conditioned seismic mapping model to generate a classified lithology map data set; apply an inversion to the classified lithology map data set to generate volumes of lithologies; generate an image that represents the volumes of lithologies; display the image.

This invention relates to a method for determining a map of expectation and/or of variance of height of liquid hydrocarbon in a geological model. The method allows analytical resolution of these maps while taking account of the uncertainties in the variables allowing this calculation such as the porosity or oil saturation of the rock and also the uncertainty in the presence of certain types of facies given by the apportionment cubes of architectural elements p.sub.AE(c) and proportion cubes for each facies, these proportions then having a triangular distribution defined by the following three values p.sub.A,AE,min(c), p.sub.A,AE,max(c) and p.sub.A,AE,mode(c). In particular, the method comprises the calculation of the sum of the plurality of architectural elements of p.sub.AE(c).Math.(p.sub.A,AE,min(c)+p.sub.A,AE,max(c)+p.sub.A,AE,mode(c)) and the determining of a value of expectation of height of liquid hydrocarbon for said column as a function of the sum determined.

Various implementations directed to determining direct hit or unintentional crossing probabilities for wellbores are provided. In one implementation, a method may include receiving wellbore trajectory data and uncertainty data for a reference wellbore section and for an offset wellbore section. The method may further include determining an analysis point in the reference wellbore section based on the received wellbore trajectory data. The method may additionally include determining segments for the offset wellbore section based on the received wellbore trajectory data. In addition, the method may include determining combined uncertainties corresponding to the analysis point and the segments based on the received uncertainty data. The method may also include determining direct hit probabilities between the analysis point and the segments based on the combined uncertainties. The method may further include drilling, or providing assistance for drilling, the reference wellbore section based on the direct hit probabilities.

Systems and methods for identifying geological core areas by using one or more rock property metrics to construct a cumulative probability distribution and variance of the rock property metrics that may be used for ranking and identifying the geological core areas.

Method for selecting an acquisition geometry for a seismic survey based on ability to resolve an a priori velocity model. Two or more candidate acquisition geometries are selected (301, 302), differing in areal coverage and cost to perform. Then compute a synthetic seismic dataset for each geometry using a detailed geometrical reference model of the subsurface (301). Invert the synthetic seismic datasets preferably using simultaneous source FWI, and preferably with Volume of Investigation constraints, to determine model updates (303, 304). Quantitatively assess the value of the additional traces in a fuller dataset relative to a subset (306), using one or more statistics based on the accuracy of the updated models, such as improved match to the reference model, better fit of seismic data, or rate of change in improvement with iterations. Inversions may be cascaded for further efficiency (314).