A glass clamping model based on microscopic displacement experiment, including a frame, a transparent silicone sleeve having a horizontal through hole, a piston, a piston cap arranged on the frame, a connecting plate, a screw compression bracket, a clamp support, a glass sheet entirety placed in the transparent silicone sleeve, a boss, a light source and a microscope. The transparent silicone sleeve is sheathed on the piston cap, the piston penetrates through the horizontal penetration hole; the connecting plate and the clamp support are respectively connected to both ends of the frame, the end of the screw compression bracket is clamped between the frame and the connecting plate, and the piston and the frame are connected with the clamp support; an emptying channel and an inlet passage are respectively arranged at both ends of the piston, and an outlet passage is arranged at an end of the piston.

A method is claimed that includes obtaining a measured present-day value of at least one parameter for each member of a set of unvalidated geological layers arranged in order of increasing depth and iteratively selecting a member of the set as a current layer. For each current layer in turn, the method further determines an estimated archaic value of at least one parameter of the current layer based on its measured present-day value by applying an alternating cycle of decompaction followed by geomechnical modeling to predict a present-day value of the parameter of the current layer based on its estimated archaic value. The method still further determines a validated archaic value of at least one parameter of each current layer based on a difference between the predicted and the measured present-day values. A non-transitory computer readable medium storing instructions for validating the archaic value for each layer is claimed.

Methods for measuring seismic velocities and for monitoring local changes in inter-well seismic velocities in real time are described. Two or more spaced-apart observation wells are provided. Seismic receiver arrays are placed in the observation wells, and a seismic source array is provided at surface locations away from the well bores and producing areas. Compression (P), vertical shear (Sv) and/or horizontal shear (Sh) seismic wave signals are generated from each element of the seismic source array, and the seismic signals arriving at the receivers in the observation wells are recorded. The virtual source method is then applied to the recorded data to compute emulated cross-well seismic signals of the virtual sources at receiver locations in one observation well propagating toward the receivers at other observation wells. Analysis of direct arrivals of emulated cross-well seismic signals can be completed to extract travel times, inter-well seismic velocities, and rock properties.

Methods for interpreting pressure transient tests and predicting future production for a well are provided. In one embodiment, a method for predicting future production includes beginning a pressure transient test within a well at a wellsite and obtaining pressure measurements of well fluid during the pressure transient test. The method can also include using the obtained pressure measurements to determine probabilistic estimates of input parameters of a pressure transient reservoir model while continuing the pressure transient test. Future production from the well can then be estimated based on the probabilistic estimates of the input parameters. Other methods and systems are also disclosed.

A reservoir simulation platform is provided. The reservoir simulation platform includes a mimetic finite discretization scheme and an operator-based linearization approach. The reservoir simulation system further includes a parallel framework for coupling the mimetic finite discretization scheme and the operator-based linearization approach.

Techniques for generating a geological model from 3D seismic data and rock property data are disclosed. Rock property data and 3D seismic data are received. Based on the rock property data and the 3D seismic data, an adaptive geological model is generated. The adaptive geological model includes a characteristic geological property. Synthetic seismic data is generated from a first region of interest of the adaptive geological model. The synthetic seismic data is adapted to facilitate a comparison between the first region of interest and a corresponding region of interest of the received 3D seismic data. The characteristic geological property is adjusted until the comparison indicates a result that is within a predetermined threshold region of the corresponding value from the rock properties. A validated geologic model is then generated.

Scattered body waves are isolated to primary, shear, and surface waves as a receiver wavefield from recorded near-surface scattered wave data generated by scatters. The isolated receiver wavefield is backward propagated through an earth model from a final to an initial state. A source wavefield and the receiver wavefields are cross-correlated. A source wavefield and the receiver wavefields are stacked, over all time steps and sources, to generate a subsurface image. A display of the subsurface image is initiated.

A method for well log data interpretation includes obtaining well data by a well log interpreter and determining, automatically by the well log interpreter, a plurality of machine-learning models corresponding to the well data based on a plurality of well data type. Additionally, the method includes determining, by the well log interpreter and in real-time, preview data regarding a well operation using the machine-learning models, and transmitting, by the well log interpreter to a user device, an interpretation report comprising the preview data. A system for well log data interpretation includes a logging system coupled to a plurality of logging tools, a logging system coupled to a plurality of logging tools, a drilling system coupled to the logging system, and a well log interpreter comprising a computer processor. The well log interpreter is coupled to the logging system and the drilling system. The well log interpreter comprising functionality for performing the well log data interpretation method.

The present invention discloses a physical embedded deep learning formation pressure prediction method, device, medium and equipment, the present invention characterizes seismic attenuation by logging impedance quality factor Q, based on the Q value and rock physics model of formation pressure, the physical mechanism of this kind of certainty replace Caianiello convolution neurons of the nonlinear activation function, using the convolution neurons, build deep learning convolution neural networks (CCNNs), can greatly increase the stress inversion precision and learning efficiency, get accurate formation pressure prediction results. Compared with the prior art, the present invention uses acoustic attenuation instead of the traditional acoustic velocity to characterize formation pressure, and solves the problem that the traditional pressure prediction method based on velocity has strong multiple solutions due to high gas content and complex structure.

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.