Abstract: A method may include obtaining reservoir data for a geological region of interest. The method may further include obtaining production data regarding one or more wells coupled to the geological region of interest. The method may further include obtaining carbon emission data for the one or more wells. The method may further include determining predicted carbon emission data and predicted production data using a machine-learning model. The method may further include determining one or more stimulation parameters for a stimulation operation based on the predicted carbon emission data and the predicted production data. The method may further include transmitting a command to a control system coupled to an injection well. The command adjusts an amount of carbon dioxide that is supplied to the injection well based on the one or more stimulation parameters.

Abstract: Methods and systems are provided for determining a sweep efficiency within a hydrocarbon reservoir. The method includes obtaining a well log for each of a plurality of wellbores penetrating the hydrocarbon reservoir, a deep sensing dataset for the hydrocarbon reservoir and determining a plurality of classified well logs, one from each well log using a first machine learning (ML) network. The method further includes determining a classified deep sensing dataset from the deep sensing dataset using a second ML network, training a third ML network to predict the sweep efficiency based, at least in part on the plurality of classified well logs and the classified deep sensing dataset at a location of each of the wellbores, and determining the sweep efficiency within the hydrocarbon reservoir using the trained third machine learning network based, at least in part, on the classified deep sensing dataset.

Abstract: A method for optimizing a wireless sensor network for monitoring hydrogen production from fire flooding involves training a machine learning model to generate an estimate of communication performance of each of a multitude of sensors. The sensors are a component of the wireless sensor network disposed in a sub-surface hydrogen reservoir, with each of the multitude of sensors configured to obtain measurements of environmental variables of the hydrogen reservoir. The method further involves minimizing a cardinality of the multitude of sensors, using the machine learning model.

Abstract: A composition of matter including a fluorescent assembly and a drilling fluid is provided. The fluorescent assembly includes a matrix material and a plurality of fluorophores held within the matrix material and has an average particle size of at least one millimeter. A method includes introducing the fluorescent assembly into a drilling fluid and circulating the drilling fluid through a well during a drilling operation that creates formation cuttings such that the fluorescent assembly interacts with the formation cuttings, creating tagged cuttings. The method further includes collecting returned cuttings from the circulating drilling fluid at a surface of the well, detecting the presence of the fluorescent assembly on the returned cuttings to identify the tagged cuttings, and correlating the tagged cuttings with a drill depth in the well at a time during the drilling operation.

Abstract: A composition of matter including a fluorescent assembly and a drilling fluid is provided. The fluorescent assembly includes a matrix material and a plurality of fluorophores held within the matrix material and has an average particle size of at least one millimeter. A method includes introducing the fluorescent assembly into a drilling fluid and circulating the drilling fluid through a well during a drilling operation that creates formation cuttings such that the fluorescent assembly interacts with the formation cuttings, creating tagged cuttings. The method further includes collecting returned cuttings from the circulating drilling fluid at a surface of the well, detecting the presence of the fluorescent assembly on the returned cuttings to identify the tagged cuttings, and correlating the tagged cuttings with a drill depth in the well at a time during the drilling operation.

Abstract: A system for drilling a wellbore is disclosed. The injection pump releases the taggant into the mud stream traveling downhole. The taggant attaches to the rock cuttings and it is detected on the surface by the taggant detector. The taggant detector provides the data relating to the taggants detected in the drilling fluid. The data is analyzed by taggant analysis and control engine, which produces an injection profile. Based on the injection profile, the IoT Controller adapts the parameters of the taggant injection pump to achieve a real-time optimization of the taggant injection.

Abstract: A method for optimizing water injection in a reservoir may include obtaining a first dataset from a first pipeline system in a first reservoir, training a first model by the first dataset, and determining reliability of the first dataset by the first model. The method may include upon determining that the first dataset is reliable, generating a first categorized dataset by the first dataset and a second model, and training a third model by the first categorized dataset. The method may include optimizing water injection control parameters of a second reservoir in accordance to a final water injection scheme by the third model.

Abstract: A method includes establishing a database including one or more characteristics of one or more reactants and a historical data subset; determining, utilizing a machine learning algorithm trained with data stored in the database, a combination of the reactants and a reaction condition to be used for synthesis of a nanofluid; and synthesizing the nanofluid based on the combination of reactants and the reaction condition.

Abstract: A method may include obtaining reservoir data for a geological region of interest. The method may further include obtaining turbine data regarding a supercritical carbon dioxide power (sCO2) turbine. The method may further include obtaining carbon emission data for a well coupled to the geological region of interest. The method may further include determining predicted production data and predicted carbon emission data using a machine-learning model, the reservoir data, the turbine data, and the carbon emission data. The method may further include transmitting a command to a control system based on the predicted production data and the predicted carbon emission data. The command may adjusts an amount of carbon dioxide that is distributed to an injection well and the sCO2 turbine. The command achieves a predetermined production rate at the well and a predetermined carbon footprint.

Abstract: A method may include obtaining reservoir data for a geological region of interest. The method may further include obtaining production data regarding one or more wells coupled to the geological region of interest. The method may further include obtaining carbon emission data for the one or more wells. The method may further include determining predicted carbon emission data and predicted production data using a machine-learning model. The method may further include determining one or more stimulation parameters for a stimulation operation based on the predicted carbon emission data and the predicted production data. The method may further include transmitting a command to a control system coupled to an injection well. The command adjusts an amount of carbon dioxide that is supplied to the injection well based on the one or more stimulation parameters.

Abstract: A composition of matter including a fluorescent assembly and a drilling fluid is provided. The fluorescent assembly includes a matrix material and a plurality of fluorophores held within the matrix material and has an average particle size of at least one millimeter. A method includes introducing the fluorescent assembly into a drilling fluid and circulating the drilling fluid through a well during a drilling operation that creates formation cuttings such that the fluorescent assembly interacts with the formation cuttings, creating tagged cuttings. The method further includes collecting returned cuttings from the circulating drilling fluid at a surface of the well, detecting the presence of the fluorescent assembly on the returned cuttings to identify the tagged cuttings, and correlating the tagged cuttings with a drill depth in the well at a time during the drilling operation.

Abstract: A method includes taking at least one image of a plurality of returned cuttings from a well using a fluorescent imaging camera, analyzing the at least one image with an imaging processing system to obtain detection data including a calculated percentage of a first emitted fluorescent light to formation cuttings, sending the detection data to an analysis and control program to correlate the returned cuttings with a depth in the well, and automatically controlling at least one drilling parameter for drilling the well based on the detection data.

Abstract: A method includes taking at least one image of a plurality of returned cuttings from a well using a fluorescent imaging camera, analyzing the at least one image with an imaging processing system to obtain detection data including a calculated percentage of a first emitted fluorescent light to formation cuttings, sending the detection data to an analysis and control program to correlate the returned cuttings with a depth in the well, and automatically controlling at least one drilling parameter for drilling the well based on the detection data.

Abstract: A system for drilling a wellbore is disclosed. The injection pump releases the taggant into the mud stream traveling downhole. The taggant attaches to the rock cuttings and it is detected on the surface by the taggant detector. The taggant detector provides the data relating to the taggants detected in the drilling fluid. The data is analyzed by taggant analysis and control engine, which produces an injection profile. Based on the injection profile, the IoT Controller adapts the parameters of the taggant injection pump to achieve a real-time optimization of the taggant injection.

Abstract: A method may include obtaining well log data regarding a geological region of interest. The well log data may correspond to logging-while-drilling (LWD) measurements or measurement-while-drilling (MWD) measurements acquired from various wellbores. The method may further include generating image data regarding the geological region of interest using the well log data and a recurrent neural network. The method may further include determining a drilling parameter for a wellbore among the wellbores in real-time using the image data. The drilling parameter may be determined while the well log data is being acquired in the wellbore. The method may further include transmitting, based on the drilling parameter, a command to a drilling system coupled to the wellbore.

Abstract: A method for depth determination of drilled rock cuttings is disclosed. The taggant is injected and transported downhole along the mud stream and attaches to the rock cuttings. Taggant impregnated cuttings are detected at the surface based on molecular weight, emission wavelengths or radio frequency characteristics for encoding the taggant. The identification code identifies the depth of the drill bit when the particular batch of the taggant is released into the mud. The detection data, in addition to mud properties, flow rates, drill volume and penetration rates, formation characteristics, and well specifications are transferred to and analyzed by a taggant analysis and control engine. The taggant analysis and control engine controls an IoT controller that adapts the parameters of the taggant injection pump to achieve an intelligent controlled release to optimize the depth characterization process.

Abstract: Systems, methods, and apparatus including computer-readable mediums for forecasting hydrocarbon reservoir properties such as well log responses and petrophysical parameters using artificial intelligence are provided. In one aspect, a method of forecasting well logs of a target well includes obtaining well data of the target well including depth and geological information and reservoir parameters and estimating jointly multiple well logs of the target well by utilizing an artificial intelligence (AI) network with the well data of the target well. The AI network is trained based on well data of existing wells that includes multiple reservoir parameters of the existing wells jointly as inputs and multiple well logs of the existing wells jointly as outputs. The estimated multiple well logs of the target well are reconciled with each other, with the well logs of the existing wells, and with geographic formation associated with the target well and the existing wells.

Abstract: Systems and methods for reservoir resistivity characterization are provided. In various aspects, an integrated framework for the estimation of Archie's parameters for a strongly heterogeneous reservoir utilizing the dynamics of the reservoir are provided. The framework can encompass a Bayesian estimation/inversion method for estimating the reservoir parameters, integrating production and time lapse formation conductivity data to achieve a better understanding of the subsurface rock conductivity properties and hence improve water saturation imaging.

Abstract: A multi-data reservoir history matching and uncertainty quantification framework is provided. The framework can utilize multiple data sets such as production, seismic, electromagnetic, gravimetric and surface deformation data for improving the history matching process. The framework can consist of a geological model that is interfaced with a reservoir simulator. The reservoir simulator can interface with seismic, electromagnetic, gravimetric and surface deformation modules to predict the corresponding observations. The observations can then be incorporated into a recursive filter that subsequently updates the model state and parameters distributions, providing a general framework to quantify and eventually reduce with the data, uncertainty in the estimated reservoir state and parameters.