A system comprising a ground drilling device with a control device, the system including an input device functionally coupled to the control device for entering at least one parameter for operating the ground drilling device. The at least one parameter may include a parameter that causes the ground drilling device to start drilling. The input device may be configured as a remote control with a feedback device, which outputs a variable that a user can perceive i) tactilely, ii) visually, and/or iii) auditorily, and that depends on a) the operation of the ground drilling device, b) the operating state of the ground drilling device, and/or c) the signal of a detection device.

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.

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.

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.

Analysis and logging of drilling mud using a mud analysis system and controlling drilling operations responsive to the mud analysis and logging. The system and methods may automatically sample and analyze drilling mud and control the drilling of a well responsive to the mud analysis. The mud analysis system may acquire measurements on a sample of the drilling mud during drilling and may send signals indicative of the drilling mud to a steering control system enabled to control the drilling.

Analysis and logging of drilling mud using a mud analysis system and controlling drilling operations responsive to the mud analysis and logging. The system and methods may automatically sample and analyze drilling mud and control the drilling of a well responsive to the mud analysis. The mud analysis system may acquire measurements on a sample of the drilling mud during drilling and may send signals indicative of the drilling mud to a steering control system enabled to control the drilling.

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.

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 method for a hyperparameter optimization for an automated ensemble machine learning model includes: generating an initial population of a plurality of machine learning (ML) models with a plurality of randomly chosen hyperparameters; calculating a loss function for each of the plurality of machine learning models; creating a new population of ML models and generating a base learner model using the hyperparameters of the best model. The method for creating the new population include the steps of: (a) selecting multiple best models with least errors as parents from a previous generation; (b) creating an offspring of the new population of ML models with a crossover probability and a mutation probability; and (c) repeating the steps (a) and (b) until a number of generations is reached and reporting the hyperparameters of the best model.