A method for the identification of the position of a first well, modelled by a distribution of poles, includes, after having determined an optimum position of each pole, determining a three-dimensional region of uncertainties around the optimum position of each pole, by applying a numerical method characterizing the differences between at least one measurement of the magnetic field and a disturbed theoretical magnetic field by varying the position of at least one pole and comparing a result of the numerical method with a threshold value. If the result is less than or equal to the threshold value, the disturbed position of the pole is considered in the region. The region is thus defined with a centre corresponding to the optimum position of the pole and the radii of which have a length at least equal to the maximum difference between the acceptable positions of the poles and the optimum position.

A computer-implemented method of enhancing a computer to estimate an uncertainty of an onset of a signal of interest in time-series noisy data. A first mathematical model of first time series data that contains only noise is calculated. A second mathematical model of second time series data that contains the noise and an onset of a signal of interest in the second time series data is calculated. A difference is evaluated between a first combination, being the first mathematical model and the second mathematical model, and a second combination, being the first time series data and the second time series data, wherein evaluating is performed using a generalized entropy metric. A specific time when an onset of the signal of interest occurs is estimated from the difference. An a posteriori distribution is derived for an uncertainty of the specific time at which the onset occurs.

Techniques for controlling a hydrocarbon production system include determining a first estimate of a prior FVC detectability probability map based on a plurality of reservoir data that includes four-dimensional (4D) seismic data of a subterranean reservoir; determining a second estimate of the prior FVC detectability probability map under seismic data noise conditions; determining an updated detectable FVC probability based on the 4D seismic data; determining an updated FVC probability based on the updated detectable FVC probability and the first and second estimates of the prior FVC detectability probability maps; and generating a control instruction for at least one of a fluid injection system or a hydrocarbon production assembly based on the updated FVC probability.

A method of predicting model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) of a geological formation under investigation, wherein said geological formation is distinguished by reservoir parameters including observable data parameters and the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) to be predicted, comprises the steps of calculating at least one model constraint (M.sub.1, M.sub.2, M.sub.3, . . . ) of the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) by applying at least one rock physics model (f.sub.1, f.sub.2, f.sub.3, . . . ) on the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ), said at least one model constraint (M.sub.1, M.sub.2, M.sub.3, . . . ) including modelled data of at least one of the data parameters, and applying an inverse model solver process on observed input data (d.sub.1, d.sub.2, d.sub.3, . . . ) of at least one of the data parameters, including calculating predicted model parameters, which comprise values of the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) which give a mutual matching of the input data and the modelled data, wherein the modelled data are provided with probability distribution functions, the inverse model solver process is conducted based on the probability distribution functions, wherein multiple predicted values of the model parameters are obtained comprising values of the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) which give the mutual matching of the input data and the modelled data, and model probabilities of the predicted model parameters are calculated in dependency on the probability distribution functions.

The subject technology relates to synthetic aperture to image leaks and sound sources. Other methods and systems are also disclosed. The subject technology includes drilling a wellbore penetrating a subterranean formation. The subject technology includes logging the wellbore using the stationary acoustic sensor and the moving acoustic sensor of the logging tool to obtain logged measurements, and obtaining an actual acoustic signal associated with a leak source in the wellbore using logged measurements. The subject technology also includes determining a synthetic acoustic signal indicating an estimated leak source in the wellbore, and determining a correlation between the synthetic acoustic signal and the actual acoustic signal. The subject technology also includes generating a probability map from the determined correlation, in which the probability map indicates a likelihood of the leak source being located at a given location in the wellbore based on the probability map.

A method and system of machine learning-augmented geophysical inversion includes obtaining measured data; obtaining prior subsurface data; (a) partially training a data autoencoder with the measured data to learn a fraction of data space representations and generate a data space encoder; (b) partially training a model autoencoder with the prior subsurface data to learn a fraction of model space representations and generate a model space decoder; (c) forming an augmented forward model with the model space decoder, the data space encoder, and a physics-based forward model; (d) solving an inversion problem with the augmented forward model to generate an inversion solution; and iteratively repeating (a)-(d) until convergence of the inversion solution, wherein, for each iteration: partially training the data and model autoencoders starts with learned weights from an immediately-previous iteration; and solving the inversion problem starts with super parameters from the previous iteration.

A method and apparatus for modeling a subsurface region, including: obtaining a training set of geologically plausible models for the subsurface region; training an autoencoder with the training set; extracting a decoder from the trained autoencoder, wherein the decoder comprises a geologic-model-generating function; using the decoder within a data-fitting objective function to replace output-space variables of the decoder with latent-space variables, wherein a dimensionality of the output-space variables is greater than a dimensionality of the latent-space variables; and performing an inversion by identifying one or more minima of the data-fitting objective function to generate a set of prospective latent-space models for the subsurface region; and using the decoder to convert each of the prospective latent-space models to a respective output-space model. A method and apparatus for making one or more hydrocarbon management decisions based on the estimated uncertainty.

A method is described for analysis of subsurface data including the use of physics-based modeling and experimental design that allows calculation of probabilities of physical subsurface properties. The method may include calculations of key controlling parameters. The method may include using multiple dimension scaling. The method may be executed by a computer system.

In an exemplary embodiment, a method is disclosed for developing an N-phasic finite element model for performing fully coupled analyses of multi-phase compositional fluid flow and heat flow in nonlinearly deforming porous solid media with time-dependent failure. The method can include formulating a finite element model of the behavior of a coupled solid-fluid formation, having complex geometry and behavior, and applying the model to derive the response of the formation in the form of one or more displacement fields for the solid phases and one or more pressure fields for the fluid phases in a zone of interest in a formation. In an exemplary embodiment, a system is disclosed for estimating the uncertainties in the derived displacement and pressure field solutions for the response of the fully coupled solid-fluid phases.

A method and apparatus for generating a fluid saturation model for a subsurface region. One example method generally includes obtaining a model of the subsurface region; for each of a plurality of fluid types: flooding the subsurface region model with the fluid type to generate a flood model; and running a trial petrophysical inversion with the flood model to generate a trial petrophysical model; identifying potential fluid contact regions in the trial petrophysical models; partitioning the subsurface region model at the identified potential fluid contact regions; and constructing the fluid saturation model from the partitioned subsurface region model.