Saudi Arabian Oil Company

Trofimuk Institute of Petroleum Geology and Geophysics

Shell Oil Comapny

Trofikmuk Institute of Petroleum Geology and Geophysics

Andrey Bakulin

Ilya Silvestrov

Dmitry Neklyudov

Kirill Gadylshin

Maxim Protasov

Robert Smith

Maxim Dmitriev

Brett Bouldin

Pavel Golikov

Michael Jervis

Ahmed Yasin Bukhamseen

Robert Turner

Ali Abdulhameed Aldawood

Stanislav Glubokovskikh

Abdullah Alramadhan

Nasher Muqbel AlbinHassan

Rayan Kanfar

Philippe Nivlet

Joost Van Der Neut

Vladimir Tcheverda

Systems and methods are disclosed. The method includes drilling, using a drill bit, a wellbore to a sequence of drilling depths within a subterranean region of interest. The method further includes, for each drilling depth, obtaining a seismic dataset generated by the drill bit, determining a coherency map using the seismic dataset, a range of seismic velocities, a range of vertical traveltimes, and a geological model, determining a locus from the coherency map, defining an objective function based on the locus, and determining a unique seismic velocity and a unique vertical traveltime based on an extremum of the objective function. The method further still includes generating a vertical traveltime, depth model using the unique seismic velocity and the unique vertical traveltime for each drilling depth, obtaining a time-domain surface seismic dataset, and determining a depth-domain surface seismic dataset using the time-domain surface seismic dataset and the vertical traveltime, depth model.

Method to determine a time-depth model

Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving prestack single sensor seismic data; representing traveltime moveout of the prestack single sensor seismic data locally as a second-order curve; calculating, using the prestack single sensor seismic data, local kinematic parameters that define the second-order curve; and performing, based in part on the local kinematic parameters, wavefield transformation on the single sensor seismic data to generate enhanced prestack single sensor seismic data.

Enhancement of single sensor seismic data

Methods, systems, and computer-readable medium to perform operations including: generating a first time-frequency spectrum of a first seismic trace from an original seismic dataset; generating a second time-frequency spectrum of a second seismic trace from an enhanced seismic dataset, where the second seismic trace corresponds to the first seismic trace; and re-combining an amplitude spectrum of the first time-frequency spectrum and a phase spectrum of the second time-frequency spectrum to generate a third time-frequency spectrum of an output trace that corresponds to the first and second seismic traces.

Enhancement of seismic data

Methods, systems, and computer-readable medium to perform operations including: generating a first time-frequency spectrum of a first seismic trace from an original seismic dataset; generating a second time-frequency spectrum of a second seismic trace from an enhanced seismic dataset, where the second seismic trace corresponds to the first seismic trace; calculating a difference between the first time-frequency spectrum and the second time-frequency spectrum to generate a noise estimate in the first seismic trace; characterizing the initial noise estimate as White Gaussian Noise (WGN); calculating, based on the characterization of the initial noise estimate, a third time-frequency spectrum of a refined noise estimate; constructing, based on the first time-frequency spectrum, the second time-frequency spectrum, and the third time-frequency spectrum, a time-frequency mask (TFM); and using the constructed TFM to generate a fourth time-frequency spectrum of an output trace that corresponds to the first and second seismic traces.

Methods, systems, and computer-readable medium to perform operations including: generating a first time-frequency spectrum of a first seismic trace from an original seismic dataset; generating a second time-frequency spectrum of a second seismic trace from an enhanced seismic dataset, where the second seismic trace; calculating a difference between the first time-frequency spectrum and the second time-frequency spectrum to generate a noise estimate in the first seismic trace; constructing, based on (i) the noise estimate, (ii) the first time-frequency spectrum, and (iii) the second time-frequency spectrum, a time-frequency mask (TFM); and using the constructed TFM to generate a third time-frequency spectrum of an output trace that corresponds to the first and second seismic traces.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a velocity model for a geological region. In one aspect, a method comprises: obtaining a current velocity model for the geological region; obtaining pre-stack and post-stack seismic data characterizing the geological region; and for each of a plurality of iterations: identifying a plurality of reflection events from the post-stack seismic data and the current velocity model; determining a respective observed travel time for each of the plurality of reflection events, comprising, for each reflection event, determining the respective observed travel time for the reflection event based at least in part on kinematic features derived from a respective seismic trace included in the pre-stack seismic data; and updating the current velocity model based at least in part on the observed travel times of the plurality of reflection events.

Seismic velocity modeling

A system and method of obtaining a high SNR seismic while-drilling data and a robust velocity profile of a geological site having a main well and at least one uphole located in the vicinity of the main well, the seismic profile being obtained from seismic waves generated by a drilling device located at the main well. The method comprises deploying at least one distributed acoustic fiber optic cable vertically in the at least one uphole, at least a portion of the fiber optic cable being positioned at a depth exceeding a predetermined depth below the surface, receiving seismic data at recording station positioned on the at least one fiber optic cable at at least the predetermined depth, generating, at a processor a high SNR seismic while-drilling signal; yielding a reliable velocity profile from the seismic data received, and determining a presence of near surface hazards from the generated high SNR while drilling seismic data.

Das system for pre-drill hazard assessment and seismic recording while drilling

A system provides seismic images of the subsurface by enhancing pre-stack seismic data. The system obtains seismic data comprising a plurality of seismic traces that are generated by measuring reflections of seismic waves emitted into a geological formation. The system sorts seismic data into at least one multidimensional gather comprising a data domain. The system determines local kinematical attributes of a seismic trace. The system forms an ensemble of seismic traces, each representing a reference point. The system applies local moveout corrections to each seismic trace of the ensemble. The system applies residual statics and phase corrections for each seismic trace that is corrected by the local moveout corrections. The system sums the seismic traces of the ensemble to obtain an output seismic trace having an increased signal-to-noise ratio (SNR) relative to the seismic trace that represents the reference point for the ensemble of seismic traces.

Providing seismic images of the subsurface using enhancement of pre-stack seismic data

Methods for determination of mechanical properties of geological formations using deep learning include receiving, by a computer system, data acquired during drilling a geological formation. The computer system generates features of the data acquired during drilling. The features are indicative of mechanical properties of the geological formation. The computer system segments the features of the data acquired during drilling into sequences readable by a trained temporal convolutional network (TCN). The computer system determines the mechanical properties of the geological formation using the TCN based on the sequences obtained from the features of the data. A display device of the computer system generates a graphical representation of the mechanical properties of the geological formation.

Determination of mechanical properties of a geological formation using deep learning applied to data acquired while drilling

Methods for determination of elastic properties of geological formations using machine learning include extracting a first feature vector from data acquired during drilling. The data includes at least drilling parameters. The first feature vector is indicative of a drilling environment classification. A machine learning classification algorithm determines the drilling environment classification based on the first feature vector. A machine learning regression algorithm is selected from multiple machine learning regression algorithms based on the drilling classification. A second feature vector is extracted from the data acquired during drilling based on the drilling classification and the selected machine learning regression algorithm. The second feature vector is indicative of elastic properties of a geological formation. The selected machine learning regression algorithm determines the elastic properties of the geological formation based on the second feature vector.

Determination of elastic properties of a geological formation using machine learning applied to data acquired while drilling

The disclosure provides systems and methods to enhance pre-stack data for seismic data analysis by: sorting the reflection seismic data acquired from cross-spread gathers into sets of data sections; performing data enhancement on the sets of data sections to generate enhanced traces by: (i) applying forward normal-moveout (NMO) corrections such that arrival times of primary reflection events become more flat, (ii) estimating beamforming parameters including a nonlinear traveltime surface and a summation aperture, (iii) generating enhanced traces that combine contributions from original traces in the sets of data sections, and (iv) applying inverse NMO corrections to the enhanced traces such that temporal rearrangements due to the forward NMO corrections are undone.

Systems and methods to enhance 3-D prestack seismic data based on non-linear beamforming in the cross-spread domain

Provided in some embodiments is a method of distributed acoustic sensing in a subterranean well. The method including advancing a torpedo into a first portion of a wellbore of a subterranean well (the torpedo including a distributed acoustic sensing (DAS) fiber-optic (FO) umbilical that is physically coupled to a surface component and adapted to unspool from the torpedo as the torpedo advances in the wellbore, and an engine adapted to generate thrust to propel the torpedo), and activating the engine to generate thrust to propel advancement of the torpedo within a second portion of the wellbore such that at least some of the DAS FO umbilical is disposed in the second portion of the wellbore.

Subterranean well torpedo distributed acoustic sensing system and method

A system, a method, and a computer program for modelling a subsurface region of the earth for hydrocarbon exploration, development, or production, including receiving a seismic prestack dataset, determining one or more multiparameter attributes on a sparse grid based on the seismic prestack dataset, associating the one or more multiparameter attributes with color image pixels, encoding the associated one or more multiparameter attributes to generate a low-resolution colored image, inpainting the low-resolution colored image by a deep neural network to build a high-resolution colored image, and decoloring the high-resolution image.

Method for fast calculation of seismic attributes using artificial intelligence

A method of evaluating processing imprint on seismic signals includes receiving a first and a second seismic dataset of a reservoir. A first and a second synthetic dataset are generated, where the second synthetic dataset is generated by multiplying at least a portion of data in the first synthetic dataset by a scaling factor. A first and a second combined dataset are generated by adding the respective seismic dataset and the respective synthetic dataset. A first and a second processed dataset are generated by applying a seismic processing step on the first and the second combined dataset, respectively. A difference factor between the first and the second processed dataset is calculated. Based on the difference factor and the scaling factor, it is determined whether the seismic processing step is able to preserve signal amplitude changes between the first and the second seismic dataset.

Evaluating processing imprint on seismic signals

Provided in some embodiments is a well torpedo system that includes a torpedo adapted to be advanced in a wellbore of a subterranean well. The torpedo including an integrated spool adapted to hold a fiber-optic (FO) umbilical including a FO line adapted to couple to a surface component, and an engine adapted to combust solid propellant to generate thrust to propel advancement of the torpedo in the wellbore.

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Dhahran, Saudi Arabia

Andrey Bakulin