Schlumberger Technology Corporation

Services Petroliers Schlumberger

Geoquest Systems B.v.

Lin Liang

Tarek M Habashy

Smaine Zeroug

Austin Boyd

Vasileios-Marios Gkortsas

Lalitha Venkataramanan

Sushil Shetty

Vanessa Simoes

Bikash Kumar Sinha

Dzevat Omeragic

Aria Abubakar

Ji Dong Li

Ting Lei

Yong-Hua Chen

Albina Mutina

Saad Omar

Michael Thambynayagam

Chang-Yu Hou

Harish Baban Datir

Qiwei Zhan

Fabio Cesar Canesin

Tao Shan

Khaled Hadj-Sassi

Timon Zimmermann

Jingxuan Liu

Embodiments presented provide for a method for providing look-ahead measurements ahead of a drill bit in hydrocarbon recovery operations. Embodiments use a pixel-based inversion workflow to produce real time prediction in horizontal and high-angle wells. The method includes obtaining data related to geological properties, performing a one dimensional look around inversion of the data to achieve one dimensional results, performing a two dimensional look ahead pixel based inversion wherein the generated results for the one dimensional look around inversion are used as a starting model to obtain a second set of results, and displaying the second set of results.

Look-around/look-ahead pixel-based inversion workflow for real time prediction ahead of the bit in horizontal and high-angle wells

A system and apparatus are described for measuring magnetic flux in a wellbore casing. In an example, the apparatus includes a primary magnet and a sensor for measuring magnetic flux in the wellbore casing created by the primary magnet. The primary magnet can have magnetic pole pieces at either end that help guide the magnetic flux into the wellbore casing. Focusing magnets can be placed on both ends of the primary magnet so that the focusing magnets are separated from the primary magnet by the magnetic pole pieces. The magnetic flux created by the focusing magnets reduces dispersion of the primary magnet's magnetic flux by forcing magnetic flux from the primary magnet that would otherwise disperse to flow toward the sensor. The sensor can output a signal with magnetic flux readings. A computing device can receive the signal and detect discontinuities in the wellbore casing.

Magnetic focusing scheme for flux leakage measurement

Facies of a formation are classified from data charactering properties of a portion of the formation as a function of depth, wherein the number of facies is determined automatically in an unsupervised manner without human input. In one embodiment, a layer-based methodology is provided that performs facies classification based on layer-based properties which are determined from well log data obtained from a plurality of different well logging tools. In another embodiment, a depth-based methodology is provided that performs facies classification based on well log data obtained depth-by-depth from a plurality of different well logging tools. The number of facies can be determined automatically without human input, for example using the Bayesian Information Criterion or a method which determines the optimal number of clusters based on the repeatability of the clustering results. In embodiments, the facies classification can be performed using the Gaussian mixture model (GMM) method.

Automated facies classification from well logs

A method and computing system device for receiving a plurality of well logs. A depth shift between at least one well log of the plurality of well logs and at least one other well log may be determined based upon, at least in part, processing the plurality of well logs with a neural network. The plurality of well logs may be matched with one another based upon, at least in part, the depth shift between the at least one well log and the at least one other well log.

System and method for automatic well log depth matching

Methods and systems are provided characterizing a formation traversed by a wellbore, wherein the formation includes at least a flushed zone and an uninvaded zone, which involve obtaining well log data based on plurality of different well log measurements of the formation at multiple depths in the wellbore. The well log data is used by a computational model that solves for a set of petrophysical parameters that characterize a portion of the formation corresponding to the multiple depths in the wellbore, wherein the set of petrophysical parameters include a cementation exponent, a saturation exponent, and a flushed zone water resistivity. The solved-for set of petrophysical parameters can be used to determine a value of water saturation of the uninvaded zone for the portion of the formation corresponding to the multiple depths in the wellbore.

Estimation of fluid saturation of a formation from integration of multiple well logs

The subject disclosure relates to the interpretation of borehole sonic data using machine learning. In one example of a method in accordance with aspects of the instant disclosure, borehole sonic data is received, and machine learning is used to interpret the borehole sonic data.

Machine learning enhanced borehole sonic data interpretation

A method, computer program product, and computing system for receiving downhole logging data for a porous media. A pore size distribution index may be estimated based upon, at least in part, nuclear magnetic resonance data (NMR) from the downhole logging data of the porous media. A relative permeability and capillary pressure curve may be generated with a feasible region of solutions based upon, at least in part, the pore size distribution index.

System and method for reducing a feasible region of solutions in a relative permeability and capillary pressure curve

Processes and systems for correlating well log data sets from well logging passes within a well bore. In some embodiments, a process for well log depth matching can include normalizing a first well log from a first logging pass obtained within a well bore and a second well log from a second logging pass obtained within the well bore, performing a pre-shift, performing feature picking to identify one or more features along the second well log, performing normalized cross-correlation based optimization between the first well log and the second well log to match the one or more features along the second well log to the same one or more features of the first well log and generating a shift table for depth shifting the one or more features of the second well log and the first well log.

Processes and systems for correlating well logging data

A method for determining properties of a laminated formation traversed by a well or wellbore employs measured sonic data, resistivity data, and density data for an interval-of-interest within the well or wellbore. A formation model that describe properties of the laminated formation at the interval-of-interest is derived from the measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model represents the laminated formation at the interval-of-interest as first and second zones of different first and second rock types. The formation model is used to derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used to refine the formation model and determine properties of the formation at the interval-of-interest. The properties of the formation may be a radial profile for porosity, a radial profile for water saturation, a radial profile for gas saturation, radial profile of oil saturation, and radial profiles for pore shapes for the first and second zones (or rock types).

Method for determining properties of a thinly laminated formation by inversion of multisensor wellbore logging data

A computer-implemented method is provided for determining properties of a formation traversed by a well or wellbore. A formation model describing formation properties at an interval-of-interest within the well or wellbore is derived from measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model is used as input to a plurality of petrophysical transforms and corresponding tool response simulators that derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used by an inversion process to refine the formation model and determine properties of the formation at the interval-of-interest. In embodiments, properties of the formation may be radial profiles for porosity, water saturation, gas or oil saturation, or pore aspect ratio.

Method for determining formation properties by inversion of multisensor wellbore logging data

Methods and related systems are described relating to an inversion approach for interpreting the geophysical electromagnetic data. The inversion can be constrained by using a multiphase fluid flow simulator (incorporating pressure data if available) which simulates the fluid flow process and calculates the spatial distribution of the water saturation and the salt concentration, which are in turn transformed into the formation conductivity using a resistivity-saturation formula. In this way, the inverted invasion profile is consistent with the fluid flow physics and moreover accounts for gravity segregation effects. Jointly with the pressure data, the inversion estimates a parametric one-dimensional distribution of permeability and porosity. The fluid flow volume is directly inverted from the fluid-flow-constrained inversion of the electromagnetic data.

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Belmont, MA, United States of America

Lin Liang