A drilling dynamics data recorder is positioned within a slot in a downhole tool. The drilling dynamics data recorder may include a sensor package, the sensor package including one or more drilling dynamics sensors and a processor, the processor in data communication with the one or more drilling dynamics sensors. The drilling dynamics data recorder may also include a memory module, the memory module in data communication with the one or more drilling dynamics sensors and a communication port, the communication port in data communication with the memory module. The drilling dynamics data recorder may further include an electrical energy source, the electrical energy source in electrical communication with the memory module, the one or more drilling dynamics sensors, and the processor.

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 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.

An equation state with a mixing rule that captures a multi-component layering within nanopores may be used to model (1) fluid mixture in the nanopores of the reservoir and (2) movement of fluid between the nanopores and large pores of the reservoir. Such modeling of fluid behavior in large pores and nanopores of the reservoir may enable more accurate simulation of the reservoir (e.g., for hydrocarbon recovery).

An equation state with a mixing rule that captures a multi-component layering within nanopores may be used to model (1) fluid mixture in the nanopores of the reservoir and (2) movement of fluid between the nanopores and large pores of the reservoir. Such modeling of fluid behavior in large pores and nanopores of the reservoir may enable more accurate simulation of the reservoir (e.g., for hydrocarbon recovery).

A setting module for setting a packer assembly in a wellbore can include a motor, a sensor, and an electronic control device. The sensor can detect pressure of fluid from a surface of the wellbore in an inner diameter of the setting module and output detected pressure to an electronic control device. The electronic control device can detect a triggering pressure sequence of the fluid from the surface and can, in response to detecting the triggered pressure sequence, output a command to the motor to drive a pump to cause a slip to move for setting the packer assembly.

Apparatus and methods can be implemented to monitor the condition of the production and intermediate casing strings in oil and gas field operations. A series of measurements can be made in a multi-pipe structure and received responses can be operated on by employing a mapping optimization procedure in which a surrogate model is updated. Estimates of one or more properties of the pipes of the multi-pipe structure can be generated using coefficients of the updated surrogate model. Additional apparatus, systems, and methods are disclosed.

Systems and methods for automated drilling control and optimization are disclosed. Training data, including values of drilling parameters, for a current stage of a drilling operation are acquired. A reinforcement learning model is trained to estimate values of the drilling parameters for a subsequent stage of the drilling operation to be performed, based on the acquired training data and a reward policy mapping inputs and outputs of the model. The subsequent stage of the drilling operation is performed based on the values of the drilling parameters estimated using the trained model. A difference between the estimated and actual values of the drilling parameters is calculated, based on real-time data acquired during the subsequent stage of the drilling operation. The reinforcement learning model is retrained to refine the reward policy, based on the calculated difference. At least one additional stage of the drilling operation is performed using the retrained model.

A system for setting a wellhead pressure after a through tubing bridge plug is installed in a wellbore includes a slickline unit, a static bottomhole pressure gauge, and a pressure setting unit. The slickline unit includes the static bottomhole pressure gauge. The static bottomhole pressure gauge includes internal memory that stores a measured bottomhole pressure. The pressure setting unit sets a wellhead pressure based on the measured bottomhole pressure stored in the internal memory of the static bottomhole pressure gauge. A related method includes installing a through tubing bridge plug in a wellbore, deploying a slickline unit into the wellbore, wherein the slickline unit includes a pressure gauge, measuring bottomhole pressure with the pressure gauge, and setting a wellhead pressure based on the measured bottomhole pressure.

A system for setting a wellhead pressure after a through tubing bridge plug is installed in a wellbore includes a slickline unit, a static bottomhole pressure gauge, and a pressure setting unit. The slickline unit includes the static bottomhole pressure gauge. The static bottomhole pressure gauge includes internal memory that stores a measured bottomhole pressure. The pressure setting unit sets a wellhead pressure based on the measured bottomhole pressure stored in the internal memory of the static bottomhole pressure gauge. A related method includes installing a through tubing bridge plug in a wellbore, deploying a slickline unit into the wellbore, wherein the slickline unit includes a pressure gauge, measuring bottomhole pressure with the pressure gauge, and setting a wellhead pressure based on the measured bottomhole pressure.