Aspects of the present disclosure relate to earth modeling using machine learning. A method includes receiving detected data at a first depth point along a wellbore, providing at least a first subset of the detected data as first input values to a machine learning model, and receiving first output values from the machine learning model based on the first input values. The method includes receiving additional detected data at a second depth point along the wellbore, providing at least a second subset of the additional detected data as second input values to the machine learning model, and receiving second output values from the machine learning model based on the second input values. The method includes combining the first output values at the first depth point and the second output values at the second depth point to generate an updated model of the wellbore, the updated model comprising an earth model.

Methods and systems are provided for characterizing a subterranean formation that involve the generation of four 3D geological model of the formation that are updated before and after an enhanced hydrocarbon production process.

Provided is a method and apparatus for predicting oil and gas yields in in-situ oil shale exploitation, the method includes: acquiring an original TOC value, a Ro value and an original HI value of a shale to be measured; and obtaining oil and gas yields in in-situ exploitation of the shale based on the original TOC value, Ro value, original HI value thereof and pre-established models for predicting oil and gas yields in in-situ oil shale exploitation, the models are pre-established based on oil and gas yield data obtained by performing a thermal simulation experiment on a plurality of different shale samples, and the original TOC value, Ro value and original HI value thereof. The above technical solution achieves a quantitative prediction of oil and gas yields in in-situ oil shale exploitation, and improves the accuracy and efficiency of prediction of oil and gas yields in in-situ oil shale exploitation.

A method for predicting an occurrence of a geological feature in a borehole image log using a backpropagation-enabled process trained by inputting a set of training images (12) of a borehole image log, iteratively computing a prediction of the probability of occurrence of the geological feature for the set of training images and adjusting the parameters in the backpropagation-enabled model until the model is trained. The trained backpropagation-enabled model is used to predict the occurrence of the geological features in non-training borehole image logs. The set of training images may include non- geological features and/or simulated data, including augmented images (22) and synthetic images (24).

Systems and methods include a computer-implemented method for predicting hydraulic fracture initiation. A fracking operations dataset is prepared using historical field information for fracking wells. A set of hyper-parameters is tuned for use in a machine learning algorithm configured to predict fracture initiation for new fracturing wells. The dataset is divided into training and test datasets. A regression algorithm is applied to train the training dataset and to validate with the test dataset. A target variable of a breakdown pressure for a new hydraulic fracturing treatment is determined. A prediction dataset is updated using at least the target variable. The training dataset is trained using a classifier of the machine learning algorithm. A prediction is made using the prediction dataset whether the new hydraulic fracturing treatment can be initiated or not. The breakdown pressure is incrementally adjusted, and the method is repeated until successful hydraulic fracture initiation is predicted.

A method of training a machine learning model to predict seafloor ripple geometry that includes receiving one or more input values, each input value based on an observation associated with ocean wave and seafloor conditions, and preprocessing the one or more input values. The method includes generating a training data set based on the preprocessed data set, splitting the training data set into a plurality of folds, and training via stacked generalization the machine learning model by performing a cross validation of each fold of training data based on at least one deterministic equilibrium ripple predictor model and on at least one machine learning algorithm. The method may include generating via the trained machine learning model, a set of one or more seafloor ripple geometry predictions, and performing Bayesian regression on the set of one or more seafloor ripple predictions to generate a probabilistic distribution of predicted seafloor ripple geometry.

The present invention relates to a method for determination of real subsoil composition or structure characterized in that the method comprises: —receiving a model representing the real subsoil, said model comprising at least one parametric volume describing a geological formation in said model, said volume having a plurality of cells; —for each cell in the plurality of cells, determining a quality index (QI.sub.cell) function of a respective position of the cell in the geological formation; —receiving a set of facies, each facies in said set being associated with a proportion and a quality index ordering in said formation; —associating a facies to each cell, said association comprising: /a/ selecting a cell with a lowest quality index within cells in the plurality of cells having no facies associated to; /b/ associating, to said cell, a facies with a lowest Quality index ordering within facies of the set of facies for which the respective proportion is not reached in the formation; /c/ reiterating steps /a/ to /c/ until all cells in the plurality of cells are associated with a facies.

A computer-implemented method for constrained multi-phase virtual flow metering and forecasting is described. The method includes predicting instantaneous flow rates and forecasting future target flow rates and well dynamics. The method includes constructing a virtual sensing model trained using forecasted target flow rates and well dynamics. The method includes building a constrained forecasting model by combining unconstrained flow forecasting models, well dynamics models, and virtual sensing models, wherein the constrained forecasting model forecasts multi-phase flow rates.

Aspects of the present disclosure relate to confidence volumes for earth modeling using machine learning. A method includes receiving detected data, wherein the detected data includes formation attributes relating to one or more depth points along or near a wellbore. The method further includes providing inputs to a plurality of machine learning models based on the detected data. The method further includes receiving output values from the plurality of machine learning models based on the inputs. The method further includes determining a measure of variance among the output values. The method further includes generating a confidence indicator related to the output values based on the measure of variance.

Improved reservoir simulation methods and systems are provided that employ a new velocity model in conjunction with a sequential implicit (SI) formulation or Sequential Fully Implicit (SF) formulation for solving the discrete form of the system of nonlinear partial differential equations. In embodiments, the new velocity model employs a fluid transport equation part based on calculation of phase velocity for a number of fluid phases that involves capillary pressure and a modification coefficient. In embodiments, the modification coefficient can be based on a derivative of capillary pressure with respect to saturation. In another aspect, the new velocity model can employ an estimate of the phase velocity of the water phase v.sub.w_est that is based on one or more derivatives of capillary pressure of the water phase as a function of water saturation.