A multivariate analysis may be used to correlate seismic attributes for a subterranean formation with petrophysical properties of the subterranean formation and/or microseismic data associated with treating, creating, and/or extending a fracture network of the subterranean formation. For example, a method may involve modeling petrophysical properties of a subterranean formation, microseismic data associated with treating a complex fracture network in the subterranean formation, or a combination thereof with a mathematical model based on measured data, microseismic data, completion and treatment data, or a combination thereof to produce a petrophysical property map, a microseismic data map, or a combination thereof; and correlating a seismic attribute map with the petrophysical property map, the microseismic data map, or the combination thereof using the mathematical model to produce at least one quantified correlation, wherein the seismic attribute map is a seismic attributed modeled for the complex fracture network.

Systems, processes, and computer-readable media for determining lithology porosity of a formation rock from surface drilling parameters without the use of wireline logging. Lithology porosity at different depths in existing may be determined from the wireline logs. The lithology porosity may be shaly sand, tight sand, porous gas, or porous wet. A lithology porosity machine-learning model may be trained and calibrating using the data from a structured data set having surface drilling parameters from the existing wells and lithology porosity classifications from the wells. The lithology porosity machine learning model may then be used to determine a lithology porosity classification for a new well without the use of wireline logging.

A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups.

A logging instrument for estimating a property of a formation is provided. The instrument includes a magnet to generate a magnetic field. The instrument also includes pulse sequencer circuitry that supplies radio frequency (RF) signals. The instrument additionally includes an antenna system configured to transmit the RF signals and to obtain nuclear magnetic resonance (NMR) measurements of the formation in response to the transmitted RF signals. In one aspect, the logging tool contains a temperature sensor configured to obtain temperature measurements of the magnet. The instrument additionally includes a control unit communicatively coupled to the temperature sensor, the antenna system and the pulse sequencer circuitry and configured to receive the temperature measurements and selectively adjust operating parameters of the pulse sequencer circuitry based on the received temperature measurements in order to maintain optimal intensity of the magnetic field.

A system, method and program product for processing borehole images to delineate between natural fractures and induced fractures. A system is disclosed that includes: an image analysis platform that inputs a noisy image from a borehole, processes the image using a set of filtering strategies, and renders a set of suggested filtered images via a user interface, the user interface including a mechanism for allowing a user to choose a selected filtered image from the set of suggested filtered images that best delineates between natural fractures and induced fractures, and wherein the image analysis platform further includes a feedback system for packaging and outputting the noisy image and selected filtered image as feedback; and a learning platform having a knowledge registration system that collects and stores training data and the feedback and in a knowledgebase, and a machine learning system that generates filtering strategies.

Methods and systems for performing log quality control on well data of non-key wells is provided. A method of identifying elastic facies in non-key wells as part of Log Quality Control (LQC) includes selecting one or more key wells, building a reference model of elastic facies using the well log data of the selected one or more key wells, propagating the reference model to well data of one or more non-key wells, benchmarking the well data of the non-key wells with the reference model, and calibrating the well data of the non-key wells with the reference model.

A global objective function is initialized to an initial value. A particular model simulation process is executed using prepared input data. A mismatch value is computed by using a local function to compare an output of the particular model simulation process to corresponding input data for the particular model simulation process. Model objects associated with the particular model simulation process are sent to another model simulation process. An optimization process is executed to predict new values for input data to reduce the computed mismatch value.

Computing systems, computer-readable media, and methods interpreting well logs, of which the method includes receiving data that comprises one or more well logs acquired using a tool disposed at a plurality of depths 423 in a bore in a subterranean environment, partitioning the data into segments, the individual segments containing data points, representing the segments as representative points in a parameter domain, determining reachability distances for the representative points in the parameter domain, initializing a cluster based on the reachability distances, identifying one or more segments as part of the cluster, and determining a physical feature represented in the one or more well logs based on the cluster.

A method of fluid substitution, wherein an initial data set is provided, wherein a substituted data set is provided, wherein a rock physics model is provided, wherein the initial data set includes initial data of a geophysical parameter and initial fluid data, and wherein the substituted data set includes substituted fluid data. The method includes using the model and the initial data set to calculate first calculated data of the geophysical parameter, using the model and the substituted data set to calculate second calculated data of the geophysical parameter, calculating the difference between the first calculated data of the geophysical parameter and the second calculated data of the geophysical parameter, and applying the difference to the initial data of the geophysical parameter to produce substituted data of the geophysical parameter.

A system, method and program product for implementing a machine learning platform that processes a data map having feature and operational information. A system is disclosed that includes an interpretable machine learning model that generates a function in response to an inputted data map, wherein the data map includes feature data and operational data over a region of interest, and wherein the function relates a set of predictive variables to one or more response variables; an integration/interpolation system that generates the data map from a set of disparate data sources; and an analysis system that evaluates the function to predict outcomes at unique points in the region of interest.