A method for a method for identifying a subsurface pore-filling fluid and/or lithology. A training set of field-acquired geophysical data and/or simulated geophysical data is provided to train a backpropagation-enabled process. The trained process is used on a field-acquired data set that is not part of the training set to infer presence of a subsurface pore-filling fluid and/or lithology.

Methods and apparatus for evaluating an earth formation for generating a numerical model comprising an expression equating a representation for the effective conductivity under Archie's law with respect to direct current with a function for the effective conductivity representative of the mixing model with respect to direct current; and solving a system of equations to obtain values for the Archie parameters including at least i) the expression; ii) a second expression equating a first order variation of the representation with a first order variation of the function with respect to water saturation of the formation (Sw); and iii) a third expression equating a first order variation of the representation with a first order variation of the function with respect to porosity of the formation (φ). The second and third expressions may equate derivatives of the representation with derivatives of the function.

Methods and systems for augmented geological service characterization are described. An embodiment of a method includes generating a geological service characterization process in response to one or more geological service objectives and a geological service experience information set. Such a method may also include augmenting the geological service characterization process by machine learning in response to a training information set. Additionally, the method may include generating an augmented geological service characterization process in response to the determination information.

A method for generating one or more reservoir models using machine learning is provided. Generating reservoir models is typically a time-intensive idiosyncratic process. However, machine learning may be used to generate one or more reservoir models that characterize the subsurface. The machine learning may use geological data, geological concepts, reservoir stratigraphic configurations, and one or more input geological models in order to generate the one or more reservoir models. As one example, a generative adversarial network (GAN) may be used as the machine learning methodology. The GAN includes two neural networks, including a generative network (which generates candidate reservoir models) and a discriminative network (which evaluates the candidate reservoir models), contest with each other in order to generate the reservoir models.

A subterranean earth formation is evaluated by running a process with a logging tool residing in a borehole in the earth formation to collect shallow measurements of a property of the formation and deep measurements of the property of the formation. An inversion is performed on the shallow measurements to produce a group of possible formation models that fit the shallow measurements. A machine-learning algorithm is applied to estimate the shallow formation structure, using the group of possible formation models that fit the shallow measurements, to produce a shallow formation structure. An inversion is performed on the deep measurements to produce a group of possible formation models that fit the deep measurements. The shallow formation structure is expanded using the group of possible formation models that fit the deep measurements to produce a deep formation structure.

Systems and methods of determining an equivalent black oil model are disclosed. In one embodiment, a method of determining an equivalent black oil model of a reservoir includes generating three-dimensional PVT properties using a compositional model, calculating original fluid in place and reservoir performance characteristics from the three-dimensional PVT properties, and converting the three-dimensional PVT properties to a two-dimensional PVT table. The method further includes, until an equivalency metric is satisfied, generating one or more grouped PVT property tables, initializing a black oil model with the one or more grouped PVT property tables, calculating estimated fluid in place and estimated reservoir performance characteristics using the black oil model, and comparing the estimated fluid in place and the estimated reservoir performance characteristics with the original fluid in place and the reservoir performance characteristics to determine whether the equivalency metric is satisfied.

Systems and methods include a method for determining a breakdown pressure for the wellbore. Input parameters are received for computing a breakdown pressure for a wellbore. A pore pressure is determined using a Stehfest method equation using a function of a time duration, a distance from the wellbore, an injection fluid compressibility, a Biot poroelastic parameter, and a modified Bessel function. A poroelastic stress is determined using a poroelastic stress equation based on the pore pressure determined for the wellbore, a Composite Simpson's Rule for numerical integration, an empirical parameter, a pore pressure, a Biot poroelastic parameter, tensile strength of rock, and a Poisson distribution. A breakdown pressure is determined using a tested time-based formula, poroelastic stress, a minimum and a maximum horizontal stress, using a formula tested against multiple wells and a distance from the wellbore in a radial direction.

Systems and methods include a method for determining a breakdown pressure for the wellbore. Input parameters are received for computing a breakdown pressure for a wellbore. A pore pressure is determined using a Stehfest method equation using a function of a time duration, a distance from the wellbore, an injection fluid compressibility, a Biot poroelastic parameter, and a modified Bessel function. A poroelastic stress is determined using a poroelastic stress equation based on the pore pressure determined for the wellbore, a Composite Simpson's Rule for numerical integration, an empirical parameter, a pore pressure, a Biot poroelastic parameter, tensile strength of rock, and a Poisson distribution. A breakdown pressure is determined using a tested time-based formula, poroelastic stress, a minimum and a maximum horizontal stress, using a formula tested against multiple wells and a distance from the wellbore in a radial direction.

System and methods of petrophysical modeling are disclosed. Training data for modeling a reservoir formation surrounding a wellbore drilled within the reservoir formation is received via a network from one or more data sources. A machine learning model is trained using symbolic regression to determine a formation model representing the reservoir formation, based on the training data received from the data source(s). At least one property of the reservoir formation is estimated, based on the formation model. A downhole operation is performed along the wellbore within the reservoir formation, based on the at least one estimated property.

Disclosed is a method of conditioning one or more parametric models. The method comprises obtaining a plurality of candidate parametric models, each describing a sequence of domains characterising a subsurface region and determining whether each sequence of domains described by one or more of said candidate parametric models is a valid sequence of domains. For each valid sequence of domains, each candidate parametric model describing that valid sequence of domains (or a subset of these models) is conditioned simultaneously, for example by using an Ensemble Kalman Filter or artificial neural network.