A method for determining a real-time pore pressure log of a well in a reservoir, including the steps: storing existing data logs of surface drilling parameters, logging while drilling (LWD), and mud gas of existing wells in a database, storing existing pore pressure logs of the existing wells in the database, wherein the existing pore pressure logs correspond to the existing data logs, determining a relationship between the existing data logs and the existing pore pressure logs, drilling a new well into the reservoir, determining new data logs of surface drilling parameters, LWD, and mud gas of the new well while drilling the new well, inputting the new data logs of the new well into the relationship while drilling the new well, determining a real-time pore pressure log of the new well by outputting an estimated pore pressure at a certain depth by the relationship while drilling the new well.

A method for optimizing drilling performance is disclosed. The method includes determining, while advancing a drill bit during a drilling operation based on drilling parameters specified by a user, a rate of penetration (ROP), acquiring, using sensors disposed on drilling equipment of a well, measurement data of each drilling equipment that represents a condition of a corresponding drilling equipment at a particular ROP during the drilling operation, determining, using an artificial intelligence method based on the measurement data, a non-linear relationship between the ROP, the drilling parameters, and the conditions of the drilling equipment, identifying a constraint specified by the user based on the conditions of the drilling equipment, determining, based on the non-linear relationship and the user specified constraint, a target value of the drilling parameters to optimize a pre-determined performance measure of the drilling operation, and further performing the drilling operation based on the target value.

A method for optimizing drilling performance is disclosed. The method includes determining, while advancing a drill bit during a drilling operation based on drilling parameters specified by a user, a rate of penetration (ROP), acquiring, using sensors disposed on drilling equipment of a well, measurement data of each drilling equipment that represents a condition of a corresponding drilling equipment at a particular ROP during the drilling operation, determining, using an artificial intelligence method based on the measurement data, a non-linear relationship between the ROP, the drilling parameters, and the conditions of the drilling equipment, identifying a constraint specified by the user based on the conditions of the drilling equipment, determining, based on the non-linear relationship and the user specified constraint, a target value of the drilling parameters to optimize a pre-determined performance measure of the drilling operation, and further performing the drilling operation based on the target value.

The disclosure provides for a method for identifying a mud motor drilling mode. The method comprises accessing historical run information stored in a memory of a controller and determining drilling measurements based on the historical run information. The method further comprises training at least one initial model with a machine learning method using the determined drilling measurements, wherein the at least one initial model comprises one or more inputs selected from a group consisting of revolutions per minute, tool-face, torque, flowrate, weight on bit, rate of penetration, differential pressure, a derivative thereof, and any combination thereof. The method further comprises utilizing the trained at least one initial model to determine the mud motor drilling mode for a mud motor.

The disclosure provides for a method for identifying a mud motor drilling mode. The method comprises accessing historical run information stored in a memory of a controller and determining drilling measurements based on the historical run information. The method further comprises training at least one initial model with a machine learning method using the determined drilling measurements, wherein the at least one initial model comprises one or more inputs selected from a group consisting of revolutions per minute, tool-face, torque, flowrate, weight on bit, rate of penetration, differential pressure, a derivative thereof, and any combination thereof. The method further comprises utilizing the trained at least one initial model to determine the mud motor drilling mode for a mud motor.

A method for drilling a new oil or gas well in a selected geographical location comprises extracting drilling modes from historic drilling data obtained from a group of drilled wells in the selected geographical location using a pattern recognition model. Each drilling mode represents a distinct pattern that quantifies at least two drilling variables at a specified drilling depth. The method also comprises selecting a sequence of drilling modes at positions along a reference well as reference drilling modes that represent more efficient values for a selection of one or more of the at least two drilling variables compared to other extracted drilling modes; associating drilling parameter settings with the reference drilling modes; and drilling the new oil or gas well applying at least some of the drilling parameter settings.

A method for drilling a new oil or gas well in a selected geographical location comprises extracting drilling modes from historic drilling data obtained from a group of drilled wells in the selected geographical location using a pattern recognition model. Each drilling mode represents a distinct pattern that quantifies at least two drilling variables at a specified drilling depth. The method also comprises selecting a sequence of drilling modes at positions along a reference well as reference drilling modes that represent more efficient values for a selection of one or more of the at least two drilling variables compared to other extracted drilling modes; associating drilling parameter settings with the reference drilling modes; and drilling the new oil or gas well applying at least some of the drilling parameter settings.

A method may include obtaining, in real-time with a drilling operation, density data regarding a drilling fluid circulating in a wellbore. The method may further include determining mud velocity data of the drilling fluid in the wellbore based on flow rate data and a borehole area of the wellbore. The method may further include determining a cuttings weight of the drilling fluid in the wellbore based on the density data. The cuttings weight and the mud velocity data may correspond to a respective segment length among various segment lengths of the wellbore. The method may further include determining, based on the cuttings weight, the mud velocity data, and a hole cleaning model, various hole cleaning efficiency (HCE) values for the segment lengths of the wellbore. The method may further include determining whether an HCE value among the HCE values fails to satisfy a predetermined criterion.

A method may include obtaining, in real-time with a drilling operation, density data regarding a drilling fluid circulating in a wellbore. The method may further include determining mud velocity data of the drilling fluid in the wellbore based on flow rate data and a borehole area of the wellbore. The method may further include determining a cuttings weight of the drilling fluid in the wellbore based on the density data. The cuttings weight and the mud velocity data may correspond to a respective segment length among various segment lengths of the wellbore. The method may further include determining, based on the cuttings weight, the mud velocity data, and a hole cleaning model, various hole cleaning efficiency (HCE) values for the segment lengths of the wellbore. The method may further include determining whether an HCE value among the HCE values fails to satisfy a predetermined criterion.

The present disclosure describes computer-implemented method for monitoring a well operation, the method including: accessing a plurality of streams of data received from multiple sources during the well operation, wherein: the multiple sources include a well testing process, a biosteering process, a geosteering process, and a drilling process, and the well operation includes sidetracking and drilling one or more laterals from a motherbore; combining the plurality of streams of data on a single montage with a common horizontal axis to track the one or more laterals from the motherbore; and presenting the single montage on a display device to an operator running the well operation.