A method includes receiving geophysical data representative of a geophysical structure; providing the geophysical data as one or more input data to a neural network; training the neural network to reconstruct the geophysical structure that was received and provide one or more uncertainty metrics for one or more features of the geophysical structure that is reconstructed; reconstructing, using the neural network that has been trained, the geophysical structure;

and determining, using the neural network that has been trained, the one or more uncertainty metrics by implementing a second drop out condition on the one or more nodes of the one or more hidden layers of the neural network. The training is performed at least partially by implementing a first drop out condition on one or more nodes of one or more hidden layers of the neural network to randomly set an output of the one or more nodes to zero.

A system includes different types of downhole sensing tools deployed in a borehole, wherein the different types of downhole sensing tools are optimized to identify a downhole condition based on a predetermined downhole evaluation plan that accounts for sensing tool availability and performance constraints. The system also includes at least one processing unit configured to analyze measurements collected by the different types of downhole sensing tools, wherein the collected measurements are analyzed together to identify the downhole condition. The system also includes at least one device that performs an operation in response to the identified downhole condition.

A system includes different types of downhole sensing tools deployed in a borehole, wherein the different types of downhole sensing tools are optimized to identify a downhole condition based on a predetermined downhole evaluation plan that accounts for sensing tool availability and performance constraints. The system also includes at least one processing unit configured to analyze measurements collected by the different types of downhole sensing tools, wherein the collected measurements are analyzed together to identify the downhole condition. The system also includes at least one device that performs an operation in response to the identified downhole condition.

A method comprises receiving a current dataset for a current time window from at least one sensor in a wellbore created in a subsurface formation, wherein the current dataset comprises values of a number of current features of the subsurface formation at a spatial location in the wellbore. The method includes selecting at least one previous time window from a number of previous time windows that includes a previously cached dataset that was detected by the at least one sensor or a different sensor in the wellbore and that spatially overlaps with the spatial location for the current dataset. The method includes merging the current dataset with the previously cached dataset to create a merged dataset. The method includes selecting a machine learning model from a plurality of machine learning models for the spatial location in the wellbore based on the merged dataset.

A method is provided for determining a lithology of a subterranean formation into which a wellbore has been drilled. The method includes receiving a set of measurement logs including one or more measurement logs, each representing a measured characteristic of the wellbore plotted according to depth. The method also includes segmenting the wellbore into regions based on identified change of trend in one or more of the measurement logs of the set, and sub-segmenting at least one region into zones based on detection of appearance or disappearance of a rock type in the cuttings percentage log, The method also includes determining, in each zone, a location, length and rock type of one or more layers based on a total percentage of each rock type in the zone in the cuttings percentage log and at least one of the additional measurement logs.

The disclosed embodiments include devices and methods to perform inversion processing of well log data. In one embodiment, a method to perform inversion processing of well log data includes obtaining an initial model of an earth formation based on a plurality of modeling parameters that includes formation parameters of the earth formation and calibration factors associated with orientations of antennas of a logging tool utilized to measure raw measurements of the earth formation. The method also includes performing a forward modeling of the modeling parameters to obtain a modeling response, and performing a joint cost function of the first modeling response and raw measurements obtained by the logging tool. The method further includes readjusting the initial model if a result of the joint cost function is not below a threshold, and providing the modeling response if the result of the joint cost function is below the threshold.

The disclosed embodiments include devices and methods to perform inversion processing of well log data. In one embodiment, a method to perform inversion processing of well log data includes obtaining an initial model of an earth formation based on a plurality of modeling parameters that includes formation parameters of the earth formation and calibration factors associated with orientations of antennas of a logging tool utilized to measure raw measurements of the earth formation. The method also includes performing a forward modeling of the modeling parameters to obtain a modeling response, and performing a joint cost function of the first modeling response and raw measurements obtained by the logging tool. The method further includes readjusting the initial model if a result of the joint cost function is not below a threshold, and providing the modeling response if the result of the joint cost function is below the threshold.

A method for determining tortuosity, e.g., in an oilfield well includes obtaining a planned trajectory for a hole, and obtaining a first survey of the hole using a sensor deployed into the hole. The first survey includes a first surveyed position at a first depth of the hole and a second surveyed position at a second depth of the hole, and no surveyed positions between the first and second depths. The method further includes simulating a second survey of the hole between the first and second depths using a model. The second survey includes a plurality of simulated positions of the hole between the first and second depths. The method includes determining that the simulated position at the second depth is proximal to the second surveyed position, and visualizing a trajectory of the hole based on the first and second surveys.

Various embodiments include methods and apparatus structured to balance electrode pairs (112, 312) of a tool (105, 305) providing voltage equivalence between the electrode pairs. An electrically conductive wire (113, 313) coupling the electrodes of an electrode pair can be arranged such that resistance of the electrically conductive wire does not adversely affect the voltage equivalence. The electrically conductive wire can also be structured to provide temperature independent balancing by arranging the electrically conductive wire with respect to a measuring node (116, 316). Additional apparatus, systems, and methods are disclosed.

Various embodiments include methods and apparatus structured to balance electrode pairs (112, 312) of a tool (105, 305) providing voltage equivalence between the electrode pairs. An electrically conductive wire (113, 313) coupling the electrodes of an electrode pair can be arranged such that resistance of the electrically conductive wire does not adversely affect the voltage equivalence. The electrically conductive wire can also be structured to provide temperature independent balancing by arranging the electrically conductive wire with respect to a measuring node (116, 316). Additional apparatus, systems, and methods are disclosed.