The downhole tool may include an electrical power source, a positive and a negative electrode, and at least one sensor. The positive and negative electrodes may be configured to generate an electric field between the positive and the negative electrode with an electrical charge from the electrical power source and discharge the electric charge through a downhole formation. The at least one sensor may be configured to detect quantum particles dislocated by at least one of the electric field and the discharged electrical charge to determine downhole information from at least one of a travel time of the quantum particles, a composition of the quantum particles, a quantity of the quantum particles, and a charge of the quantum particles.

Process-mining software is disclosed for generating a process flow for forming a wellbore at a wellsite. The process-mining software can receive data from sensors at a wellsite. The process-mining software can determine wellbore operations performed at the wellsite, based on the received data, using a predefined algorithm. The process-mining software can generate an event log based on the determined wellbore operations. The process-mining software can then generate a process flow based on the event log. The process-mining software can output the process flow for use in forming one or more wellbores.

A method can include receiving multi-channel time series data of drilling operations; training a deep neural network (DNN) using the multi-channel time series data to generate a trained deep neural network as part of a computational simulator where the deep neural network includes at least one recurrent unit; simulating a drilling operation using the computational simulator to generate a simulation result; and rendering the simulation result to a display.

The present invention discloses an intelligent recognition method for while-drilling safety risks based on a convolutional neural network. The method includes the following steps: 1, processing while-drilling safety risk parameter features and data, and establishing a correlation analysis model for monitoring-while-drilling parameters by using a Pearson coefficient correlation analysis method; 2, processing while-drilling safety monitoring data, analyzing a time span of each sample, constructing training sample data and test sample data, and preprocessing the samples; 3, designing a while-drilling safety risk recognition network structure; and 4, recognizing while-drilling safety risks by the trained safety risk recognition network. The method of the present invention is applied to monitoring-while-drilling engineering, which can greatly improve the drilling efficiency and a reservoir drilling rate, reduce a complex accident rate and cost in drilling, provide a powerful safety guarantee for drilling work, meet the current urgent demands for cost reduction and efficiency enhancement in drilling to a certain extent, and also provide a new idea for the development of intelligent drilling technologies in China.

Systems and methods include a method for providing an integrated advanced visualization tool for geosteering underbalanced coiled tubing drilling (UBCTD) operations. Drilling operation data is received from different sources in real time during a drilling operation. The drilling operation data includes geological formation information recorded during the drilling operation, micropalaeontological test results of the drilling operation, drilling parameters being used during the drilling operation, cumulative productivity index calculations, and reservoir pressure information of reservoirs encountered during the drilling operation. The drilling operation data is analyzed to correlate elements of the drilling operation data by time and cumulative depth. A graph is generated in real time that includes multiple plots correlated as a function of cumulative depth over time.

A system can assign a value to one or more sustainability factors for a wellbore operation based on historical data. The system can determine, for each of the one or more sustainability factors, a weight. The system can determine a sustainability index corresponding to a predicted carbon footprint for the wellbore operation based on the weight and the value for each of the one or more sustainability factors. The system can output a command for adjusting the wellbore operation based on the sustainability index.

There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in the exploration, determination, production and recovery of natural resources, including energy sources, and the monitoring, control and analysis of processes and activities.

A formation test probe and a formation test system and method for implementing a self-drilling probe are disclosed. In some embodiments, a test probe includes a body having a channel therethrough to a frontside port, and further includes drill-in tubing disposed within the channel and having a front tip that is extensible from the frontside port. An exciter is disposed within the body in contact with the drill-in tubing and operably configured to induce resonant vibration in the drill-in tubing during a drill-in phase of a formation test cycle.

An automated slide drilling system (ASDS) may be used with a drilling rig system to control slide drilling. The ASDS may autonomously control slide drilling without user input during the slide drilling. The ASDS may further support a transition from rotary drilling to slide drilling to rotary drilling without user input during the transitions. The ASDS may also support user input and user notifications for various steps to enable manual or semi-manual control of slide drilling by a driller or an operator.

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.