A system is for processing seismic data for a geologic formation generated by an array of acoustic transducers responsive to an acoustic source. The system may include a seismic data storage device, and a processor cooperating with the seismic data storage device to generate correlations of data from the array of acoustic transducers based upon a current estimate for at least one of density and velocity of the geologic formation, and perform a linear optimization based upon a plurality of different combinations of the correlations to determine a given subset of correlations having a highest objective function associated therewith. The processor may also update the current estimate for at least one of density and velocity of the geologic formation based upon the given subset of correlations.

An acoustic logging method that may comprise acquiring waveforms for multiple acoustic wave modes as a function of tool position in a borehole; deriving position-dependent mode dispersion curves from the waveforms; accessing a computed library of dispersion curves for a vertical shear slowness (s) and a Thomsen gamma () of a given acoustic wave mode as a function of frequency; interpolating dispersion curves in the computed library to an assumed known compressional wave slowness, a borehole radius, a formation density, a mud density, and a mud slowness; computing an adaptive weight; and inverting dispersion curve modes jointly for a shear wave anisotropy, a vertical shear wave slowness, an inverted mud slowness, and an inverted mud density as a function of depth. An acoustic logging system may comprise a logging tool, a conveyance attached to the logging tool, at least one sensor, and at least one processor.

A method of predicting model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) of a geological formation under investigation, wherein said geological formation is distinguished by reservoir parameters including observable data parameters and the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) to be predicted, comprises the steps of calculating at least one model constraint (M.sub.1, M.sub.2, M.sub.3, . . . ) of the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) by applying at least one rock physics model (f.sub.1, f.sub.2, f.sub.3, . . . ) on the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ), said at least one model constraint (M.sub.1, M.sub.2, M.sub.3, . . . ) including modelled data of at least one of the data parameters, and applying an inverse model solver process on observed input data (d.sub.1, d.sub.2, d.sub.3, . . . ) of at least one of the data parameters, including calculating predicted model parameters, which comprise values of the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) which give a mutual matching of the input data and the modelled data, wherein the modelled data are provided with probability distribution functions, the inverse model solver process is conducted based on the probability distribution functions, wherein multiple predicted values of the model parameters are obtained comprising values of the model parameters (R.sub.1, R.sub.2, R.sub.3, . . . ) which give the mutual matching of the input data and the modelled data, and model probabilities of the predicted model parameters are calculated in dependency on the probability distribution functions.

Techniques for estimating and visually presenting formation slowness are disclosed herein. The techniques include receiving acoustic signal responses from adjacent formations at a plurality of depths in a borehole environment, mapping a distribution of the acoustic signal responses at each depth according to slowness and a frequency values, determining at least one confidence interval to define a coherence threshold for the distribution of the acoustic signal responses at each depth, generating a variable density log for each depth based on the distribution of acoustic signals responses that satisfy the confidence interval for one or more frequency ranges, determining a formation slowness value for each depth based on the variable density log for the each depth, and presenting a semblance map that includes a slowness axis, a depth axis, the formation slowness value for each depth, and at least a portion of the distribution of acoustic signal responses at each depth.

A method of performing a fracture operation at a wellsite is provided. The wellsite has a fracture network therein with natural fractures. The method involves stimulating the wellsite by injecting an injection fluid with proppant into the fracture network, obtaining wellsite data comprising natural fracture parameters of the natural fractures and obtaining a mechanical earth model of the subterranean formation, defining the natural fractures based on the wellsite data by generating one or more realizations of the natural fracture data based on a statistical distribution of natural fracture parameters, meters, generating a statistical distribution of predicted fluid production by generating a hydraulic fracture growth pattern for the fracture network over time based on each defined realization and predicting fluid production from the formation based on the defined realizations, selecting a reference production from the generated statistical distribution, and optimizing production and uncertainty by adjusting the stimulating operations based on the selecting.

Methods and mediums for estimating stimulated reservoir volumes are disclosed. Some method embodiments may include obtaining microseismic event data acquired during a hydraulic fracturing treatment of the formation, the data including event location and at least one additional attribute for each microseismic event within the formation; filtering the microseismic events based on the at least one additional attribute; determining a density of filtered microseismic events; weighting the filtered microseismic events based on the density; and determining a stimulated reservoir volume estimate based on filtered and weighted microseismic events.

Systems, methods, and computer programs for monitoring production of fluids from a subterranean formation includes receiving, from a first sensor array at a first time, a first set of electromagnetic signals generated by an electroseismic or seismoelectric conversion of seismic signals caused, at least in part, by the production of fluid from the subterranean formation; receiving, from the first sensor array at a second time, a second set of electromagnetic signals generated by an electroseismic or seismoelectric conversion of seismic signals caused, at least in part, by the production of fluid from the subterranean formation; and determining one or more reservoir properties based, at least in part, on the first and second sets signals received from the first sensor array. The first sensor array are arranged to monitor the production operation.

A computer-implemented method for updating subsurface models including: using an offset continuation approach to update the model, and at each stage defining a new objective function where a maximum offset for each stage is set, wherein the approach includes, performing a first stage iterative full wavefield inversion with near offset data, as the maximum offset, to obtain velocity and density or impedance models, performing subsequent stages of iterative full wavefield inversion, each generating updated models, relative to a previous stage, wherein the subsequent stages include incrementally expanding the maximum offset until ending at a full offset, wherein a last of the stages yields finally updated models, the subsequent stages use the updated models as starting models, and the full wavefield inversions include constraining scales of the velocity model updates at each stage of inversion as a function of velocity resolution; and using the finally updated models to prospect for hydrocarbons.

Apparatus and methods to generate a two-dimensional image of the well can include control of measurements in a well to generate a log of the well and use of such measurements and/or logs with a log of a reference well. The boundary positions of layers of the well can be correlated with corresponding boundary positions from the reference well. Such apparatus and methods or similar apparatus and methods can be implemented in a variety of applications.

The present disclosure generally relates to a real-time synthetic logging method for optimizing one or more operations in a well. The method generally includes receiving measurements of one or more parameters in real time while performing operations in the well, the measurements being captured without using tools that include active nuclear sources. The method further includes providing the measurements as input to a machine learning algorithm (MLA) that is trained using historical or training well data. The method further includes generating, using the MLA and based on the measurements, a synthetic mechanical property log of the well. The method further includes generating, based on the synthetic mechanical property log, optimized parameters for at least one operation selected from the following list: drilling the well in real-time; steering the well in real-time; and stimulating a reservoir in real-time.