Schlumberger Technology Corporation

Westerngeco L.l.p.

Services Petroliers Schlumberger

Schlumberger Holdings Limited

Prad Research and Development Limited

Michael David Prange

William John Bailey

Hugues A Djikpesse

Benoit Couet

Peter Gerhard Tilke

Mohamed-Rabigh Khodja

Clinton Dane Chapman

Andrew John Carnegie

Yi-Qiao Song

Tarek M Habashy

Andrew Emil Pomerantz

Josselin Kherroubi

Kashif Rashid

Raj Banerjee

Paul Ryan Craddock

Jeffrey Spath

Isabelle Le Nir

Vladimir L'vovich Druskin

Michael Thambynayagam

Henry Menkiti

Zikri Bayraktar

Trevor Graham Tonkin

Darren Lee Aklestad

Alberto Malinverno

Martin Crick

Vijaya Halabe

Darrell Coles

Marie LeFranc

Morten Kristensen

Philippe Marza

Ibrahim Pamir Bursin

Stephen Flew

Michael A Elliott

Dadi Wang

Darren Lee Acklestad

Marie Emeline Cecile LeFranc

Sebastien Duchenne

Laure Pizzella

Embodiments of the present disclosure are directed towards systems and methods for automated stratigraphy interpretation from borehole images. Embodiments may include constructing, using at least one processor, a training set of synthetic images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images. Embodiments may further include automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using one or machine learning techniques. Embodiments may also include automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments.

Method for automated stratigraphy interpretation from borehole images

A method can include receiving realizations of a model of a reservoir that includes at least one well where the realizations represent uncertainty in a multidimensional space; selecting a portion of the realizations in a reduced dimensional space to preserve an amount of the uncertainty; optimizing an objective function based at least in part on the selected portion of the realizations; outputting parameter values for the optimized objective function; and generating at least a portion of a field operations plan based at least in part on at least a portion of the parameter values.

Optimization under uncertainty for integrated models

Methods may include creating a fracture set from a collection of intersecting fractures in a borehole image log recorded within a subterranean formation; classifying the fracture set into groups of fully and partially intersecting fractures; calculating one or more of the elongation ratio and the rotation angle of the partially intersecting fractures; determining a probability of full intersection of fractures from the fracture set; and determining a fracture size or a parametric distribution of fracture sizes from the fracture set using the calculated one or more of the elongation ratio and the rotation angle and the determined probability of full intersection of formation fractures within the borehole.

Method for characterizing the geometry of subterranean formation fractures from borehole images

Managing oilfield operations include obtaining a subsurface model including a fracture design model having an fracture property with an uncertain value. A set of representative values that represent uncertainty in the fracture property is obtained and used to solve an oilfield optimization problem with a control variable to obtain a solution. The solution includes an optimal value for the control variable. Based on the solution, an oilfield design is generated and stored.

Multistage oilfield design optimization under uncertainty

A method for estimating a value of a kerogen property in a subsurface formation where the value of the kerogen property is unknown. The method includes: measuring spectral intensity values over an infrared (IR) spectral range for a selected sample from the subsurface formation; determining a range of values representing the measured spectral intensity values corresponding to a vibrational mode attributable to kerogen in the selected sample, the range of values including values representing uncertainty in the measured spectral intensity over the portion of the spectral range; and inputting values from the range of values into a stochastic or simple regression model to determine an estimated value of the kerogen property in the selected sample.

Method for estimating a value of a kerogen property in subsurface formations

A Bayesian methodology is described for designing experiments or surveys that are improved by utilizing available prior information to guide the design toward maximally reducing posterior uncertainties in the interpretation of the future experiment. Synthetic geophysical tomography examples are used to illustrate benefits of this approach.

Guided bayesian experimental design

Apparatus and methods for controlling equipment to recover hydrocarbons from a reservoir including constructing a collection of reservoir models wherein each model represents a realization of the reservoir and comprises a subterranean formation measurement, estimating the measurement for the model collection, and controlling a device wherein the controlling comprises the measurement estimate wherein the constructing, estimating, and/or controlling includes a rolling flexible approach and/or a nearest neighbor approach.

Optimization of a multi-period model for valuation applied to flow control valves

Apparatus and methods of categorizing a subterranean formation including collecting data related to the formation, performing an algorithm comprising guided Bayesian survey design, identifying data observations to collect, and collecting additional data observations related to the formation. In some embodiments, the performing comprises forming a matrix or loading a matrix or both. Apparatus and methods of categorizing a subterranean formation including collecting data related to the formation, performing an algorithm comprising guided Bayesian survey design, identifying data observations to use, and processing the data observations wherein the observations require less processing time than if no algorithm were performed.

Large survey compressive designs

The present invention relates to a method and system for quantifying the value-of-information (VoI) of a proposed and future well test where multiple uncertainties associated with the reservoir properties and/or measurement and/or interpretation may be present.

Valuing future well test under uncertainty

Complex-valued sensitivity data structures corresponding to respective candidate survey settings are provided, where the sensitivity data structures relate measurement data associated with a target structure to at least one parameter of a model of the target structure. Based on the sensitivity data structures, a subset of the candidate survey settings is selected according to a criterion for enhancing resolution in characterizing the target structure.

Selecting a survey setting for characterizing a target structure

Methods and related systems are described for extracting information about a system of nuclear spins including: performing a plurality of Nuclear Magnetic Resonance (NMR) measurements on the system of nuclear spins; acquiring NMR data from each of the plurality of NMR measurements; performing data inversion using an random-sampler to generate an ensemble of spectra so as to extract information about the system of nuclear spins; and analyzing the performed random-sampler inversion results to extract information about the system of nuclear spins.

Monte Carlo method for laplace inversion of NMR data

A system for automatically optimizing a Field Development Plan (FDP) for an oil or gas field uses a fast analytic reservoir simulator to dynamically model oil or gas production from the entire reservoir over time in an accurate and rapid manner. An objective function defining a Figure of Merit (FoM) for candidate FDPs is maximized, using an optimization algorithm, to determine an optimized FDP in light of physical, engineering, operational, legal and engineering constraints. The objective function for the Figure of Merit, e.g., net present value (NPV) or total production for a given period of time, relies on a production forecast from the fast analytic reservoir simulator for the entire FDP.

Automated field development planning

Guided Bayesian experimental design

A method of determining a selection of well measurements and/or their respective control parameters is described based on a global target having predetermined sensitivities to a multitude of uncertainties associated with input variables to a model including the step of ranking measurements and control parameters in accordance with their capability to reduce the uncertainties of input variables identified as most sensitive input variables through simulations using the model or a reduced variant of the model.

Method for selecting well measurements

The invention relates to a method of performing an oilfield operation of an oilfield having at least one well having a wellbore penetrating a subterranean formation for extracting fluid from an underground reservoir therein. The method steps include analyzing the oilfield operation to generate a decision tree comprising a first decision and a second decision, wherein a first outcome of the first decision dictates acquiring information relevant to the second decision, formulating a figure of merit of the oilfield operation according to the decision tree based on the information and uncertainties associated with the oilfield, determining a value of the figure of merit by modeling the oilfield operation using statistical sampling, and performing the oilfield operation upon making the first decision based on the value of the figure of merit.

Valuing future information under uncertainty

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Somerville, MA, United States of America

Michael David Prange