A depth positioning method to position a production logging tool (1) and a measurement log in a hydrocarbon well (3) in production obtained by means of the tool, the depth positioning method comprises: generating (S1, S2, S3, S1′, S2′, S3′, S11, S12, S13) a set of magnetic measurements (MAG1, MAG) of a depth portion of the hydrocarbon well from a first passive magnetic sensor along the depth portion of the hydrocarbon well, the set of magnetic measurements comprising magnitude and/or direction measurements of the magnetic field that forms a characteristic magnetic field pattern representative of a surrounding magnetic environment of the hydrocarbon well all along the depth portion; comparing (S4, S4′, S14) the set of magnetic measurements (MAG1, MAG) to another set of magnetic measurements (MAG_R, MAG2), the other set of magnetic measurements being a reference set of magnetic measurements generated either by a same or similar passive magnetic sensor deployed and run in the hydrocarbon well earlier, or by a second passive magnetic sensor spaced from the first passive magnetic sensor from a defined distance (DS) deployed and run in the hydrocarbon well simultaneously; and determining (S4, S4′, S14) the maximum of correlation between the set of magnetic measurements (MAG1, MAG) and the reference set of magnetic measurements (MAG_R, MAG2), the maximum being related to identifiable characteristic magnetic field pattern over a part of the depth portion.

A method of wellbore analysis using TM01 and TE01 modes of radar waveforms can include transmitting, at a first time, a radar waveform from a wellhead into a tubing disposed in a wellbore positioned in a reservoir. The radar waveform is either a TM01 mode or a TE01 mode waveform. The tubing includes a fluid, and the surface of the wellbore includes the wellhead. At a second time, a reflected waveform generated by reflecting the transmitted radar waveform on a fluid surface of the fluid is received at the wellhead. A fluid level of the fluid is determined based on the time difference between the first time and the second time, and on a transmission speed of the radar waveform from the wellhead to the fluid surface. The fluid level is a distance between the wellhead and the fluid surface of the fluid.

A method of wellbore analysis using TM01 and TE01 modes of radar waveforms can include transmitting, at a first time, a radar waveform from a wellhead into a tubing disposed in a wellbore positioned in a reservoir. The radar waveform is either a TM01 mode or a TE01 mode waveform. The tubing includes a fluid, and the surface of the wellbore includes the wellhead. At a second time, a reflected waveform generated by reflecting the transmitted radar waveform on a fluid surface of the fluid is received at the wellhead. A fluid level of the fluid is determined based on the time difference between the first time and the second time, and on a transmission speed of the radar waveform from the wellhead to the fluid surface. The fluid level is a distance between the wellhead and the fluid surface of the fluid.

A system for locating a casing collar includes a drill pipe sub-assembly with a drill pipe segment and detection apparatus. The detection apparatus includes a bypass port disposed in a wall of the drill pipe segment; an annular sleeve which directs fluid through the bypass port and into a drill pipe segment interior portion; an actuator which opens and closes the bypass port; and two magnetized coils which generate an electromagnetic field. The actuator closes the bypass port in response to a predetermined voltage generated by the magnetized coils when they displace past a casing collar. Also included are a weight loss detection device which detects a loss of weight in the drill pipe segment, and a depth determination device which determines a depth of the casing collar, based on detected loss of weight. Also disclosed and described are a related method and drill pipe sub-assembly.

A system for locating a casing collar includes a drill pipe sub-assembly with a drill pipe segment and detection apparatus. The detection apparatus includes a bypass port disposed in a wall of the drill pipe segment; an annular sleeve which directs fluid through the bypass port and into a drill pipe segment interior portion; an actuator which opens and closes the bypass port; and two magnetized coils which generate an electromagnetic field. The actuator closes the bypass port in response to a predetermined voltage generated by the magnetized coils when they displace past a casing collar. Also included are a weight loss detection device which detects a loss of weight in the drill pipe segment, and a depth determination device which determines a depth of the casing collar, based on detected loss of weight. Also disclosed and described are a related method and drill pipe sub-assembly.

A method can include receiving block position data of a rig prior to addition of a length of pipe to a drillstring, where the drillstring is disposed at least in part in a borehole and supported by the rig; receiving block position data of the rig after addition of the length of pipe to the drillstring; and controlling position of the drillstring with respect to time using the rig and at least a portion of the block position data for landing a drill bit of the drillstring on a bottom of the borehole.

A method can include receiving block position data of a rig prior to addition of a length of pipe to a drillstring, where the drillstring is disposed at least in part in a borehole and supported by the rig; receiving block position data of the rig after addition of the length of pipe to the drillstring; and controlling position of the drillstring with respect to time using the rig and at least a portion of the block position data for landing a drill bit of the drillstring on a bottom of the borehole.

A method for performing wellbore correlation across multiple wellbores includes predicting a depth alignment across the wellbores based on a geological feature of the wellbores. Predicting a depth alignment includes selecting a reference wellbore, defining a control point in a reference signal of a reference well log for the reference wellbore, and generating an input tile from the reference signal, the control points, and a number of non-reference well logs corresponding to non-reference wellbores. The well logs include changes in a geological feature over a depth of a wellbore. The input tile is input into a machine-learning model to output a corresponding control point for each non-reference well log. The corresponding control point corresponds to the control point of the reference log. Based on the corresponding control points output from the machine-learning model, the non-reference well logs are aligned with the reference well log to correlate the multiple wellbores.

A method for performing wellbore correlation across multiple wellbores includes predicting a depth alignment across the wellbores based on a geological feature of the wellbores. Predicting a depth alignment includes selecting a reference wellbore, defining a control point in a reference signal of a reference well log for the reference wellbore, and generating an input tile from the reference signal, the control points, and a number of non-reference well logs corresponding to non-reference wellbores. The well logs include changes in a geological feature over a depth of a wellbore. The input tile is input into a machine-learning model to output a corresponding control point for each non-reference well log. The corresponding control point corresponds to the control point of the reference log. Based on the corresponding control points output from the machine-learning model, the non-reference well logs are aligned with the reference well log to correlate the multiple wellbores.

Methods for determining water-filled porosity using a general volumetric mixing law and the measurements of a dielectric tool are described. The water-filled porosity estimates are used to obtain water salinity estimates and the optimal parameters of the volumetric mixing law. These estimates are in turn used to generate novel quality indicators.