Schlumberger Technology Corporation

Boston University

Sensia LLC

Schlumberger Canada Limited

Services Petroliers Schlumberger

Sandip Bose

Smaine Zeroug

Henri-Pierre Valero

Bikash Kumar Sinha

Jiaqi Yang

Ting Lei

Shuchin Aeron

Pu Wang

Abderrhamane Ounadjela

Maja Skataric

Tarek M Habashy

Ralph Michael D'Angelo

Jacques A Jundt

Ram Sunder Kalyanaraman

Lingchen Zhu

Venkatesh Saligrama

Ramachandra Ganesh Shenoy

Yang Liu

Bo Fan

Marthe Souquiere

Ma Luo

Olivier Moyal

Oliver Clinton Mullins

Li Chen

Chung Lung Chang

Lalitha Venkataramanan

Benoit Couet

Denise E Freed

Jagdish H Shah

Nikita Chugunov

Sandeep Verma

Peter Gerhard Tilke

Thomas J Plona

Qiuhua Liu

Sylvain Thierry

Philippe F Salamitou

Christoph Klieber

Mikhail Lemarenko

Vasileios-Marios Gkortsas

Erik Wielemaker

Peter Tilke

Michael Kane

Melanie Jensen

Ramachandra Shenoy

Marwan Moufarrej

Xiaojun Huang

Xuqing Wu

Bikash K Sinha

A method for determining a shear slowness of a subterranean formation includes receiving waveforms data acquired by receivers in an acoustic measurement tool in response to energy emitted by at least one dipole source. The waveforms are processed to extract a formation flexural acoustic mode and a tool flexural acoustic mode. The processing includes transforming the time domain waveforms to frequency domain waveforms, processing the frequency domain waveforms with a Capon algorithm to compute a two-dimensional spectrum over a chosen range of group slowness and phase slowness values; and processing the two-dimensional spectrum to extract the multi-mode slowness dispersion. The method further includes selecting a plurality of slowness-frequency pairs from the formation flexural mode of the extracted multi-mode dispersion wherein each slowness-frequency pair comprises a slowness value at a corresponding frequency and processing the selected slowness frequency pairs to compute the shear slowness of the subterranean formation.

Determining shear slowness from dipole source-based measurements acquired by a logging while drilling acoustic measurement tool

Methods and systems are provided that characterize and evaluate well integrity of a cased well using unsupervised machine learning of acoustic data. Sonic waveform data for acoustic signals received by the receiver array of a sonic logging tool is collected and processed to determine a high-dimensional representation of the sonic waveform data. The high-dimensional representation is input to an unsupervised machine learning system to determine a low-dimensional representation of the sonic waveform data. A clustering method is applied to the low-dimensional representation to identify a set of clusters therein. At least one well integrity property of the depth interval of the cased well is determined based on the set of clusters. In embodiments, the at least one well integrity property can characterize cement condition in an annulus of the cased well as a function of azimuth and depth and can be used to evaluate cement integrity in a depth interval of the cased well.

Methods for characterizing and evaluating well integrity using unsupervised machine learning of acoustic data

Processes and systems for determining asphaltene equilibrium between two or more downhole geographic locations are provided. In some embodiments, the process can include measuring one or more fluid properties of a plurality of fluid samples at varying downhole depths to generate a one or more downhole fluid analysis measurement data points; selecting an asphaltene diameter distribution based on prior knowledge; utilizing the asphaltene diameter distribution to fit a first set of one or more equation of state curves to the one or more downhole fluid analysis measurement data points to define a first model of fitted equation of state curves and to determine one or more posterior distributions of asphaltene diameters; and determining if the varying downhole depths are in an asphaltene equilibrium by determining whether the one or more posterior distributions of asphaltene diameters is consistent with that of asphaltenes in equilibrium.

Processes and systems for determining if downhole fluids are in equilibrium or non-equilibrium

A method, computer program product, and computing system for generating high resolution slowness logs. The method computer program product, and computing system includes receiving a plurality of sonic logs from at least one sensor array and generating at least one high-resolution slowness log from the plurality of sonic logs based upon, at least in part, monopole and dipole data from the plurality of sonic logs.

System and method for generating slowness logs in thinly laminated formations

Aspects described herein provide for methods and apparatus for characterizing azimuthal heterogeneities in a barrier installed outside an outer casing in a borehole traversing a formation in a cased hole configuration including an inner and outer casing. The approach is based on specific attributes in sonic signals acquired with an azimuthal and axial array receiver system located inside the inner casing. The methods include slowness-time-coherence (STC) processing based on specific arrivals identified in data acquired by axial arrays associated with multiple azimuthal sections of the receiver system. The specific arrivals contain STC signatures that can be examined in terms of coherence amplitude and localization within STC maps. Based on specific attributes in the sonic signals, an azimuthal coverage of the outer casing annular contents can be created.

Through tubing near-field sonic measurements to map outer casing annular content heterogeneities

A downhole tool including at least one transmitter, a receiver array, and a controller. Each receiver element of the receiver array is configured to apply variable amplification and a variable time delay to detected acoustic waveforms, wherein the variable amplification is controlled by an amplification factor assigned to the given receiver element, and wherein the variable time delay is controlled by a time delay assigned to the given receiver element. The controller assigns a set of amplification factors and time delays to the receiver elements of the receiver array and combines signals resulting from the application of the variable amplification and the variable time delay to the detected acoustic waveforms such that sensitivity of the receiver elements of the receiver array is focused at a desired zone-of-interest in a wellbore corresponding to the set of amplification factors and time delays assigned to the receiver elements of the receiver array.

Guided mode beamforming for probing open-hole and cased-hole well environments

Methods are provided for determining properties of an anisotropic formation (including both fast and slow formations) surrounding a borehole. A logging-while-drilling tool is provided that is moveable through the borehole. The logging-while drilling tool has at least one dipole acoustic source spaced from an array of receivers. During movement of the logging-while-drilling tool, the at least one dipole acoustic source is operated to excite a time-varying pressure field in the anisotropic formation surrounding the borehole. The array of receivers is used to measure waveforms arising from the time-varying pressure field in the anisotropic formation surrounding the borehole. The waveforms are processed to determine a parameter value that represents shear directionality of the anisotropic formation surrounding the borehole.

Methods and systems for determining fast and slow shear directions in an anisotropic formation using a logging while drilling tool

A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

Methods of analyzing cement integrity in annuli of a multiple-cased well using machine learning

A method, computer program product, and computing system are provided for receiving sonic data associated with an inner casing of a well. Predicted ultrasonic data associated with an outer casing of the well may be generated based upon, at least in part, a nonlinear regression model and the received sonic data associated with the inner casing of the well.

Method for generating predicted ultrasonic measurements from sonic data

A method and downhole tool is provided that uses beamforming to localize acoustic energy at a desired zone-of-interest within a wellbore traversing a subterranean formation. The tool has an array of transmitter elements configured to emit guided mode acoustic signals at variable amplitude and variable time delay, which are individually controlled by an amplitude factor and time delay assigned to respective transmitter elements. A set of amplitude factors and time delays can be assigned to the transmitter elements of the transmitter array such that the transmitter elements produce a focused acoustic beam at the desired zone-of-interest by combination of guided mode acoustic signals transmitted by the transmitter elements.

Cement in a wellbore is evaluated by using cement evaluation tools to obtain ultrasonic image information and flexural wave image information. Flexural wave imager waveforms are applied to a trained machine learning system that predicts the presence or lack thereof of a clinging P as a function of depth and azimuth, while the ultrasonic imager impedance maps are applied to a trained machine learning system that identifies galaxy patterns for depths and azimuths, indicating the presence of a third interface echo (TIE) close to the casing. Locations of clinging Ps are compared to locations of galaxy patterns to identify when a TIE could cause a false clinging P determination. Where a disambiguated clinging P is found, contaminated cement is identified that may also locate the top of the cement in the annulus.

Evaluating casing cement using automated detection of clinging compression wave (P) arrivals

Methods arc provided for using sonic tool data to investigate a multi-string wcllbore. The sonic data is processed to obtain indications of phase slowness dispersions for multiple locations in the wellbore. The dispersions are aggregated. The aggregated dispersions are compared with a plurality of cut-off mode templates to identify the presence of cut-off modes or the lack thereof in the aggregated phase slowness dispersions. Features of the multi-string wellbore are identified based on the presence of the cut-off modes or the lack thereof. In another method, the sonic data is processed to obtain indications as a function of depth of at least one of an energy spectrum, a semblance projection, a slowness dispersion projection, an attenuation dispersion projection, and a wavenumber dispersion projection. The indications are inspected to locate a shift at a particular depth indicat- ing a transition in at least oneannulus of the multi-string wellbore.

Well integrity analysis using sonic measurements over depth interval

The present disclosure provides methods and systems for analyzing cement integrity in a depth interval of a wellbore having a multiple string casing with an innermost annulus disposed inside at least one outer annulus. The method includes processing ultrasonic data obtained from ultrasonic measurements on the interval of the wellbore to determine properties of the innermost annulus. The method also includes processing sonic data obtained from sonic measurements on the interval of the wellbore to extract features of the sonic data. The features of the sonic data are input to a machine learning processing to determine properties of both the innermost annulus and the least one outer annulus. Additional processing of ultrasonic and sonic data can also be used to determine properties of both the innermost annulus and the least one outer annulus. These properties can be used to analyze cement integrity in the depth interval of the wellbore.

Method for analyzing cement integrity in casing strings using machine learning

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Brookline, MA, United States of America

Sandip Bose