Accordingly, there are disclosed herein geologic modeling methods and systems employing reference-based inversion of seismic image volumes. An illustrative method embodiment includes: (a) obtaining a measured seismic image volume; (b) determining a reference seismic image volume based on a reference model; (c) deriving a synthesized seismic image volume from a geologic model; (d) detecting at least one geologic model region where the synthesized seismic image volume and the measured seismic image volume are mismatched; (e) finding a reference model region where the reference seismic image volume best matches the measured seismic image volume; (f) replacing content of the at least one geologic model region with content of the reference model region to obtain an improved geologic model; and (g) outputting the improved geologic model.

A system, computer-readable medium, and method for determining a potential drilling location, of which the method includes obtaining data representing a subterranean domain. The data includes at least seismic data. The method also includes inverting the seismic data, creating a petroleum systems model of the subterranean domain based at least in part on a result of inverting the seismic data, simulating a dynamic reservoir model of the subterranean domain based at least in part on the petroleum systems model, and identifying the potential drilling location based on a combination of the inverting of the seismic data, creating the petroleum systems model, and simulating the dynamic reservoir model.

A method for improved prediction and enhancement of hydrocarbon recovery from ultra-tight/unconventional reservoirs for both the primary production and any subsequent solvent huff‘n’puff periods based on facilitating the diffusion process may include steps of defining one or more initial properties of a reservoir and integrating characterization data of the reservoir; defining a wellbore trajectory for each of at least one well and one or more parameters associated with a completion/reservoir stimulation design; specifying operating conditions for a current development cycle; performing diffusion-based dynamic fracture/reservoir simulation for calculating hydrocarbon recovery and efficiency of a hydrocarbon process; and; determining whether to commence or continue enhanced oil recovery (EOR) or enhanced gas recovery (EGR) cycles.

Systems and methods include a method for generating a high-resolution advanced three-dimensional (3D) transient model that models multiple wells by integrating pressure transient data into a static geological model. A crude 3D model is generated from a full-field geological model that models production for multiple wells in an area. A functional 3D model is generated from the crude 3D model. An intermediate 3D model is generated by calibrating the functional 3D model with single-well data. An advanced 3D transient model is generated by calibrating multi-well data in the functional 3D model.

A method and apparatus for generating a fluid saturation model for a subsurface region. One example method generally includes obtaining a model of the subsurface region; for each of a plurality of fluid types: flooding the subsurface region model with the fluid type to generate a flood model; and running a trial petrophysical inversion with the flood model to generate a trial petrophysical model; identifying potential fluid contact regions in the trial petrophysical models; partitioning the subsurface region model at the identified potential fluid contact regions; and constructing the fluid saturation model from the partitioned subsurface region model.

Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.

A method for determining change in stress in a reservoir formation includes inducing a pressure pulse in a first well hydraulically connected by a fracture to the reservoir formation. A stress-related attribute of the fracture is determined from reflection events detected in pressure measurement made in the first well as a result of the inducing the pressure pulse. The inducing and determining are repeated to estimate changes in the stress-related attribute with respect to time. A method for determining and localizing type of interaction between a treated well and an observation well by monitoring pressure and fracture changes in the observation well.

A system includes different types of downhole sensing tools deployed in a borehole, wherein the different types of downhole sensing tools are optimized to identify a downhole condition based on a predetermined downhole evaluation plan that accounts for sensing tool availability and performance constraints. The system also includes at least one processing unit configured to analyze measurements collected by the different types of downhole sensing tools, wherein the collected measurements are analyzed together to identify the downhole condition. The system also includes at least one device that performs an operation in response to the identified downhole condition.

Systems and methods include a computer-implemented method for predicting fluid holdups along the borehole or the pipe on surface and to perform fluid typing and fluid properties characterization. Acoustic waves are transmitted by an array of acoustic wave transducers. Each transducer is configured to transmit acoustic waves at a different frequency. The acoustic waves are received by an array of acoustic wave receivers fixed on the bow centralizer on the tool used in the borehole. Each receiver is configured to receive only the given frequency of a given transducer, forming a receiver-transducer pair for the given frequency. Acoustic speeds measured at each given frequency and analyzed. A model is generated based on the analyzing. The model is configured to predict fluid holdups across the borehole and to perform fluid typing and fluid properties characterization in one phase, two phase, and three phase applications of gas, oil, and water.

Systems and methods are provided for modeling a reservoir. An exemplary method includes: receiving a reservoir model associated with a reservoir workflow process; modifying the reservoir model associated with the reservoir workflow process using an optimum time-step strategy; extracting features from the reservoir model along with first time-step sizes; generating a first set of data for devising a training set using the first time-step sizes; determining whether the selected amount of the first set of data reaches a predetermined level; triggering a real-time training using the training set and a machine learning (ML) algorithm; generating an ML model having second time-step sizes using the training set; selecting the first step-sizes or the second step-sizes based on the confidence level; sending the selected step-sizes to a simulator for processing; receiving results from the simulator that used the selected step-sizes; and determining whether results from the simulator require updating the training set.