Apparatus, systems, and methods for ranging operate to use a wireline active ranging system to initially determine a relative distance and relative direction from a first well (e.g., ranging well) to a second well (e.g., target well) and an EM azimuthal logging tool to maintain or adjust the distance from the target well while drilling the ranging well. Additional apparatus, systems, and methods are disclosed.

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.

A boring tool is movable through the ground. A transmitter supported by the boring tool transmits an electromagnetic homing signal. A portable device monitors the electromagnetic homing signal and receives the electromagnetic homing signal in a homing mode for guiding the boring tool to a target position. A processor generates steering commands for guiding the boring tool based on a bore plan in a steering mode such that at least some positional error is introduced without using the electromagnetic homing signal. Switching from the steering mode to the homing mode is based on monitoring of the electromagnetic homing signal as the boring tool approaches the portable device to then guide the boring tool to the target position location in compensation for the positional error. Intermediate target positions are described as well as guiding the boring tool based on the homing signal so long as the portable device receives the signal.

Methods, computing systems, and computer-readable media for dynamically presenting different types of data in an interface at different stages of a plan based on the monitoring the progression of the plan. The method includes receiving a selection of a goal for an operation, determining a plan based on the selected goal, determining different types of data to present at different stages of the plan, continuously receiving data representing one or more measurements taken during the operation, identifying a progression of the plan based on the continuously receiving the data, and dynamically presenting the different types of data in an interface at different stages of the plan based on the identifying the progression of the plan. The dynamically presenting the different types of data directs a user's attention to the different types of data relevant at the different stages of the plan.

A system and methods for petro-steering methodologies are provided. An exemplary method obtains rock fabric data, and integrate rock fabric data with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. Gas rates and steering values are predicted across UBCT wells based on the patterns.

A system and methods for petro-steering methodologies are provided. An exemplary method obtains rock fabric data, and integrate rock fabric data with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. Gas rates and steering values are predicted across UBCT wells based on the patterns.

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.

System and methods for geosteering inversion are provided. Downhole tool responses are predicted for different points along a planned path of a wellbore during a downhole operation, based on each of a plurality of inversion models. Measurements of the downhole tool's actual responses are obtained as the wellbore is drilled over the different points during a current stage of the operation. The inversion models are clustered based on a comparison between the actual and predicted tool responses and a randomly selected centroid for each cluster. The inversion models are re-clustered using an average inversion model determined for each cluster as the centroid for that cluster. At least one of the re-m clustered inversion models is used to perform inversion for one or more subsequent stages of the downhole operation along the planned wellbore path. The planned wellbore path is adjusted for the subsequent stage(s) of the downhole operation.

System and methods for geosteering inversion are provided. Downhole tool responses are predicted for different points along a planned path of a wellbore during a downhole operation, based on each of a plurality of inversion models. Measurements of the downhole tool's actual responses are obtained as the wellbore is drilled over the different points during a current stage of the operation. The inversion models are clustered based on a comparison between the actual and predicted tool responses and a randomly selected centroid for each cluster. The inversion models are re-clustered using an average inversion model determined for each cluster as the centroid for that cluster. At least one of the re-m clustered inversion models is used to perform inversion for one or more subsequent stages of the downhole operation along the planned wellbore path. The planned wellbore path is adjusted for the subsequent stage(s) of the downhole operation.