A method for waveform inversion, the method including receiving observed data d, wherein the observed data d is recorded with sensors and is indicative of a subsurface of the earth; calculating estimated data p, based on a model m of the subsurface; calculating, using a trained neural network, a misfit function J.sub.ML; and calculating an updated model m.sub.t+1 of the subsurface, based on an application of the misfit function J.sub.ML to the observed data d and the estimated data p.

A method for waveform inversion, the method including receiving observed data d, wherein the observed data d is recorded with sensors and is indicative of a subsurface of the earth; calculating estimated data p, based on a model m of the subsurface; calculating, using a trained neural network, a misfit function J.sub.ML; and calculating an updated model m.sub.t+1 of the subsurface, based on an application of the misfit function J.sub.ML to the observed data d and the estimated data p.

Described embodiments generally relate to a computer-implemented method for modelling a subsurface formation. The method comprises receiving measurement data related to the subsurface formation, the measurement data comprising a plurality of data points; determining at least one rock physics model, each rock physics model relating to a rock type; assigning each data point of the measurement data to at least one initial rock class membership; fitting each determined rock physics model of the at least one rock physics model to the data points of the measurement data to produce at least one fitted rock physics model; reassigning each data point to at least one rock class based on the fitted rock physics models; determining whether a convergence criterion has been met; and responsive to the convergence criterion not being met, repeating the fitting and reassigning steps.

Described embodiments generally relate to a computer-implemented method for modelling a subsurface formation. The method comprises receiving measurement data related to the subsurface formation, the measurement data comprising a plurality of data points; determining at least one rock physics model, each rock physics model relating to a rock type; assigning each data point of the measurement data to at least one initial rock class membership; fitting each determined rock physics model of the at least one rock physics model to the data points of the measurement data to produce at least one fitted rock physics model; reassigning each data point to at least one rock class based on the fitted rock physics models; determining whether a convergence criterion has been met; and responsive to the convergence criterion not being met, repeating the fitting and reassigning steps.

Acoustic characterization and mapping of flow from a perforation zone of a well. As a wireline probe containing acoustic sensors moves through the well, the acoustic sensors record acoustic pressure measurements of flow for each perforation in the well casing. The acoustic data is recorded and compiled into a three-dimensional flow model showing flow of hydrocarbons within and/or out of perforation tunnels. The three-dimensional flow models generated can be combined with historical data to form four-dimensional models illustrating flow over time, and both the three and four-dimensional models can be used to determine effectiveness of perforation charges as well as future flow from the well.

Acoustic characterization and mapping of flow from a perforation zone of a well. As a wireline probe containing acoustic sensors moves through the well, the acoustic sensors record acoustic pressure measurements of flow for each perforation in the well casing. The acoustic data is recorded and compiled into a three-dimensional flow model showing flow of hydrocarbons within and/or out of perforation tunnels. The three-dimensional flow models generated can be combined with historical data to form four-dimensional models illustrating flow over time, and both the three and four-dimensional models can be used to determine effectiveness of perforation charges as well as future flow from the well.

The disclosure addresses overdosing of antiscalants by providing monitoring of scaling tendency to provide advance warning in real time for adjusting antiscalant amounts and other related processes. A method, system, and a scaling tendency monitor are disclosed that provide online monitoring at a well for reducing scaling in production piping, such as production tubing and production lines, while reducing overfeed of antiscalants. The disclosed scaling tendency monitoring gives a real-time warning of increases in scaling tendency, before the scaling actually happens in the production piping. In one example, the scaling tendency monitor includes: (1) conduit, (2) a stresser configured to apply at least one type of scaling stress to tapped produced water flowing through the conduit, and (3) an analyzer configured to determine a change in scaling tendency of the tapped produced water after application of the one or more scaling stress.

The disclosure addresses overdosing of antiscalants by providing monitoring of scaling tendency to provide advance warning in real time for adjusting antiscalant amounts and other related processes. A method, system, and a scaling tendency monitor are disclosed that provide online monitoring at a well for reducing scaling in production piping, such as production tubing and production lines, while reducing overfeed of antiscalants. The disclosed scaling tendency monitoring gives a real-time warning of increases in scaling tendency, before the scaling actually happens in the production piping. In one example, the scaling tendency monitor includes: (1) conduit, (2) a stresser configured to apply at least one type of scaling stress to tapped produced water flowing through the conduit, and (3) an analyzer configured to determine a change in scaling tendency of the tapped produced water after application of the one or more scaling stress.

This disclosure describes systems, methods, and apparatuses for preventing wireline sticking during hydraulic fracturing operations, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations measured in fracking fluid in the wellhead, fluid line, or standpipe into an electrical signal in a time domain; a memory configured to store the electrical signal; a converter configured to access the electrical signal from the memory and convert the time domain electrical signal into a frequency domain spectrum; a machine-learning system configured to classify the current frequency domain spectrum as associated with increasing wireline friction, the machine-learning system trained on previous frequency domain spectra measured during previous wireline operations and previously classified by the machine-learning system; and a user interface configured to return an indication of the increasing wireline friction to an operator of the hydraulic fracturing operations.

This disclosure describes systems, methods, and apparatuses for preventing wireline sticking during hydraulic fracturing operations, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations measured in fracking fluid in the wellhead, fluid line, or standpipe into an electrical signal in a time domain; a memory configured to store the electrical signal; a converter configured to access the electrical signal from the memory and convert the time domain electrical signal into a frequency domain spectrum; a machine-learning system configured to classify the current frequency domain spectrum as associated with increasing wireline friction, the machine-learning system trained on previous frequency domain spectra measured during previous wireline operations and previously classified by the machine-learning system; and a user interface configured to return an indication of the increasing wireline friction to an operator of the hydraulic fracturing operations.