The invention relates to a construction machine, in particular drilling rig, comprising a mast, along which a tool is movably mounted, wherein the tool is suspended on a cable which can be actuated by means of cable winch for raising and lowering the tool, which cable winch is driven by an electric motor, as a winch drive, in a drive mode, wherein the electric motor is designed for operating in a recuperation mode in which the electric motor preferably generates electrical energy when the tool is lowered and outputs said electrical energy to an energy store, and a controller, which is designed to actuate the electric motor, depending on a specifiable target value, in such a way that the cable is tensioned both in the drive mode and in the recuperation mode of the electric motor.

Systems and methods of controlling drilling operations including Sliding With Indexing For Toolface (SWIFT) and Variable Weight Drilling (VWD) techniques. The methods and systems may include systems and devices for controlling the drilling operations, including systems and devices capable of automatically determining drilling parameters and setting operating parameters for drilling in a wellbore. The systems and methods may also determine a change in weight on bit and/or toolface, determine a timeframe for a weight on bit to be delivered to the bit, and/or determine a spindle change to modify the toolface. The systems and methods may also send control signals to apply the spindle change and/or block velocity change to correct any detected or anticipated toolface error.

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.

Systems and methods presented herein are configured to train a neural network model using a first set of photographs, wherein each photograph of the first set of photographs depicts a first set of objects and include one or more annotations relating to each object of the first set of objects; to automatically create mask images corresponding to a second set of objects depicted by a second set of photographs; to enable manual fine tuning of the mask images corresponding to the second set of objects depicted by the second set of photographs; to re-train the neural network model using the second set of photographs, wherein the re-training is based at least in part on the manual fine tuning of the mask images corresponding to the second set of objects depicted by the second set of photographs; and to identify one or more individual objects in a third set of photographs using the re-trained neural network model.

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.

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.

A system for generating artificial permeability during the completion phase may include sensors that capture real-time data pertaining to a reservoir parameter, a formation parameter, or a tool condition parameter. The system may also include a drilling tool arranged in a wellbore to drill, using side drill bits, paths into a formation zone based on a drilling program. The system may also include an access module to access the real-time data captured by the sensors inside the well. The system may also include one or more processors configured to arrange the drilling tool in the wellbore, in proximity to the formation zone. The one or more processors may also be configured to cause an operation of the drilling tool based on the drilling program, update the drilling program based on the real-time sensor data, and cause an adjustment of the drilling of the paths based on the updated drilling program.

A system for generating artificial permeability during the completion phase may include sensors that capture real-time data pertaining to a reservoir parameter, a formation parameter, or a tool condition parameter. The system may also include a drilling tool arranged in a wellbore to drill, using side drill bits, paths into a formation zone based on a drilling program. The system may also include an access module to access the real-time data captured by the sensors inside the well. The system may also include one or more processors configured to arrange the drilling tool in the wellbore, in proximity to the formation zone. The one or more processors may also be configured to cause an operation of the drilling tool based on the drilling program, update the drilling program based on the real-time sensor data, and cause an adjustment of the drilling of the paths based on the updated drilling program.

Disclosed herein are a method and system for controlling a drill rig by utilising a drill control module. A drill control module for controlling functionality of a drill rig comprises: a processor; a storage medium for storing a computer program having instructions that when executed on the processor perform the method steps of: drilling a hole by repeatedly: activating a hammer drilling mode for a predefined interval parameter, said activated hammer drilling mode causing a drill bit of said drill rig to impact a surface using a hammer drilling technique; and activating a pause interval for a predefined interval time, during which said drill bit reduces engagement with said surface.