Abstract: Described herein are systems and techniques for improving an accuracy of determinations made using data sensed in a wellbore or in a laboratory. Nuclear magnetic resonance (NMR) sensing devices may be used to collect data in a wellbore or lab. NMR sensing devices include a magnet (e.g., a permanent magnet or electromagnet) that provides a magnetic field that aligns the spins of protons in substances near the NMR sensing device. The magnetic field strength provided by the magnet of the NMR sensing device affects the sensitivity of the NMR sensing device and affects frequencies that the NMR sensing device effectively uses when the NMR sensing device operates. Systems and techniques of the present disclosure may measure concentrations of lithium in brine deposits when identifying particular brine deposits that include sufficient lithium concentrations to justify extracting lithium from those particular brine deposits.

Abstract: Described herein are systems and techniques for improving accuracies of determinations made using a nuclear magnetic resonance (NMR) sensing device when the NRM sensing device collects data in a wellbore. In certain instances, determinations made from NMR measurement data may not correspond to measurements made by other types of sensing equipment. For example, determinations of pore sizes made from evaluating sets of capillary pressure data may not correspond to determinations made from data sensed during an NMR test. Since the accuracy of determinations regarding wellbore petrophysical parameters made from data sensed by sensing equipment can affect the efficiency and profitability of a wellbore operation, and since NMR sensing devices are more deployable in a wellbore than other forms of test equipment, systems and techniques of the present disclosure are directed to improving the accuracy of petrophysical parameters determinations made from data sensed by NMR sensing devices.

Abstract: Systems and methods are provided for using sequential residual symbolic regression for petrophysical modeling. An example method can include receiving training data for modeling one or more petrophysical parameters based on reservoir formation data; performing symbolic regression using the training data to obtain a first set of symbolic regression models; determining a first residual based on the training data and a first symbolic regression model from the first set of symbolic regression models; performing symbolic regression using the first residual to obtain a second set of symbolic regression models; and updating the first symbolic regression model based on a second symbolic regression model from the second set of symbolic regression models to yield a first revised symbolic regression model.

Abstract: Described herein are systems and techniques for improving an accuracy of determinations made using data sensed in a wellbore or in a laboratory. Nuclear magnetic resonance (NMR) sensing devices may be used to collect data in a wellbore or lab. NMR sensing devices include a magnet (e.g., a permanent magnet or electromagnet) that provides a magnetic field that aligns the spins of protons in substances near the NMR sensing device. The magnetic field strength provided by the magnet of the NMR sensing device affects the sensitivity of the NMR sensing device and affects frequencies that the NMR sensing device effectively uses when the NMR sensing device operates. Systems and techniques of the present disclosure may measure concentrations of lithium in brine deposits when identifying particular brine deposits that include sufficient lithium concentrations to justify extracting lithium from those particular brine deposits.

Abstract: A NMR tool for use in a wellbore in a subterranean region includes a magnet assembly to produce a magnetic field in a volume in the subterranean region; an antenna to produce an excitation in the volume, and to receive a plurality of spin echo waveforms from the volume; and a computing system coupled to the antenna and configured to: apply a first acquisition window having a first duration to the spin echo waveforms to generate a corresponding first echo train including a first plurality of NMR echo signal amplitudes; apply a second acquisition window having a second duration different than the first duration, to at least some of the spin echo waveforms to generate a corresponding second echo train including a second plurality of NMR echo signal amplitudes; and determine a relaxation parameter based on a single inversion of the first and second echo trains.

Abstract: Carbon Capture, Utilization, and Storage (CCUS) is a relatively new technology directed to mitigating climate change by reducing greenhouse gas emissions. Current and new government requirements require proof that carbon dioxide (CO2) is either sequestered in a stable form or safely stored for long periods of time. In instances when the CO2 is sequestered through mineral formation, the need for long-term monitoring can be reduced, as the stability of the sequestered CO2 is inherent based on a chemical change in subterranean rocks. The reactions between CO2 and rock formations are influenced by numerous factors, including temperature, pressure, fluid composition, and the mineralogy of the formation. Furthermore, these reactions occur over large spatial areas and long timescales, making them difficult to monitor directly.

Abstract: System and methods of petrophysical modeling are disclosed. Measurements of formation parameters are received from one or more measurement tools during a first stage of a downhole operation within a reservoir formation. A correlation between each of the formation parameters and a target parameter of the formation is determined based on the measurements. One or more formation parameters are selected as input parameters for a symbolic regression model, based on the correlation. A symbolic regression model is trained to generate candidate formation models representing the target parameter, based on the selected input parameters. One or more optimizations are applied to the candidate models to determine a target petrophysical model of the formation. Values of the target parameter are estimated for at least one formation layer, based on the target petrophysical model. A second stage of the downhole operation is performed within the formation layer(s) based on the estimated values.

Abstract: Systems and techniques are provided for integrating laboratory generated nuclear magnetic resonance (NMR) data and NMR logging data. An example method can include obtaining NMR logging data describing one or more downhole NMR measurements captured during a drilling operation in a borehole; modifying the NMR logging data to be compatible with a temperature correction algorithm, yielding modified NMR logging data, the temperature correction algorithm having been determined based on laboratory generated NMR data; and applying the temperature correction algorithm to the modified NMR logging data, yielding temperature corrected NMR logging data.

Abstract: Noise in drilling operation measurements can be eliminated or reduced using machine learning. For example, a system described herein can receive one or more measured signals in a logging-while-drilling process for drilling a wellbore. The system can determine a coupling factor for noise in the one or more measured signals. The system can generate a corrected signal by removing the noise multiplied by the coupling factor from the one or more measured signals. The system can output the corrected signal for use in drilling operations in the wellbore.

Abstract: A measurement tool may be positioned downhole in a wellbore for measuring formation properties and drilling mud properties during a drilling operation. The measurement tool may include a body and an antenna. The body may include magnets for generating a magnetic field and a transmitter for transmitting a radiofrequency pulse. The antenna may be positioned proximate to the body to measure properties using nuclear magnetic resonant frequencies. The antenna may measure formation properties in a first volume of a formation using a first frequency. The antenna may measure drilling mud properties in a second volume in a borehole using a second frequency.

Abstract: A NMR tool for use in a wellbore in a subterranean region includes a magnet assembly to produce a magnetic field in a volume in the subterranean region; an antenna to produce an excitation in the volume, and to receive a plurality of spin echo waveforms from the volume; and a computing system coupled to the antenna and configured to: apply a first acquisition window having a first duration to the spin echo waveforms to generate a corresponding first echo train including a first plurality of NMR echo signal amplitudes; apply a second acquisition window having a second duration different than the first duration, to at least some of the spin echo waveforms to generate a corresponding second echo train including a second plurality of NMR echo signal amplitudes; and determine a relaxation parameter based on a single inversion of the first and second echo trains.

Abstract: System and methods for nuclear magnetic resonance (NMR) fluid substitution are provided. NMR logging measurements of a reservoir rock formation are acquired. Fluid zones within the reservoir rock formation are identified based on the acquired measurements. The fluid zones include water zones comprising water-saturated rock and at least one oil zone comprising rock saturated with multiphase fluids. Water zones having petrophysical characteristics matching those of the oil zone(s) within the formation are selected. NMR responses to multiphase fluids resulting from a displacement of water by hydrocarbon in the selected water zones are simulated. A synthetic dataset including NMR T2 distributions of multiphase fluids is generated based on the simulation. The synthetic dataset is used to train a machine learning (ML) model to substitute NMR T2 distributions of multiphase fluids with those of water. The trained ML model is applied to the NMR logging measurements acquired for the oil zone(s).

Abstract: System and methods of petrophysical modeling are disclosed. Measurements of formation parameters are received from one or more measurement tools during a first stage of a downhole operation within a reservoir formation. A correlation between each of the formation parameters and a target parameter of the formation is determined based on the measurements. One or more formation parameters are selected as input parameters for a symbolic regression model, based on the correlation. A symbolic regression model is trained to generate candidate formation models representing the target parameter, based on the selected input parameters. One or more optimizations are applied to the candidate models to determine a target petrophysical model of the formation. Values of the target parameter are estimated for at least one formation layer, based on the target petrophysical model. A second stage of the downhole operation is performed within the formation layer(s) based on the estimated values.

Abstract: A method and microfluidic device to perform reservoir simulations using pressure-volume-temperature (“PVT”) analysis of wellbore fluids.

Abstract: System and methods of petrophysical modeling are disclosed. Measurements of formation parameters along a planned path of a wellbore are received during a current stage of a downhole operation. A correlation coefficient between each of the formation parameters and at least one target parameter for the reservoir formation is determined based on the received measurements. Input parameters are selected from among the formation parameters for a symbolic regression model, based on the correlation coefficient calculated for each formation parameter. A symbolic regression model is trained to generate a target petrophysical model, based on the selected input parameters and the corresponding measurements received from the downhole tool. One or more properties of the formation are estimated for a subsequent stage of the downhole operation, based on the generated petrophysical model. The subsequent stage is performed along the wellbore, based on the one or more estimated properties of the reservoir formation.

Abstract: A method includes generating a temperature-corrected nuclear magnetic resonance (NMR) measurement-derived value corresponding to a target temperature using a correlation model that is based on a difference between the target temperature and a sample temperature. The method also includes determining a formation property based on the temperature-corrected NMR measurement-derived value corresponding to the target temperature.

Abstract: A method for measuring a carbon capture and sequestration site. The method may comprise acquiring one or more core samples from a carbon capture and sequestration site, performing a nuclear magnetic resonance (NMR) measurement on the one or more core samples to form a first NMR measurementperforming a surface roughness measurement on the one or more core samples to determine a Rs,before wherein the Rs,before is a surface roughness of the one or more core samples before the one or more core samples are aged in a cell, and determining at least one property of the one or more core samples from at least the first NMR measurement and the Rs,before.

Abstract: System and methods for nuclear magnetic resonance (NMR) fluid substitution are provided. NMR logging measurements of a reservoir rock formation are acquired. Fluid zones within the reservoir rock formation are identified based on the acquired measurements. The fluid zones include water zones comprising water-saturated rock and at least one oil zone comprising rock saturated with multiphase fluids. Water zones having petrophysical characteristics matching those of the oil zone(s) within the formation are selected. NMR responses to multiphase fluids resulting from a displacement of water by hydrocarbon in the selected water zones are simulated. A synthetic dataset including NMR T2 distributions of multiphase fluids is generated based on the simulation. The synthetic dataset is used to train a machine learning (ML) model to substitute NMR T2 distributions of multiphase fluids with those of water. The trained ML model is applied to the NMR logging measurements acquired for the oil zone(s).

Abstract: A method and microfluidic device to perform reservoir simulations using pressure-volume-temperature (“PVT”) analysis of wellbore fluids.

Abstract: An NMR based wettability index determination method for CO2-liquid-solid system for CO2 and liquid phase wettability assessment may comprise acquiring 1H NMR relaxation time measurements, analyzing brine signals for the comparable brine-filled pores from various step, applying a wettability index model constructed with NMR alone and calibrated with another wettability measurement, and applying the wettability index model to interpret wettability of CO2-containing rock system from corresponding NMR measurements.