Saudi Arabian Oil Company

Schlumberger Technology Corporation

Baker Hughes Oilfield Operations LLC

Halliburton Energy Services, Inc.

Weatherford International, Inc.

Saudi Aramco

Services Petroliers Schlumberger

Schlumberger Holdings Limited

Shouxiang Mark Ma

Mustafa Ali Al-Huwaider

Wael Abdallah

Ping Zhang

Chengbing Liu

Guodong Jin

Salah Mohammed Al-Ofi

Gabor Hursan

Nader H Hwety

Harold Pfutzner

Darryl E Trcka

Andrea Valori

Hasan Kesserwan

Anas Mohammedali Almarzooq

Chicheng Xu

Yahia Ahmed Eltaher

Salem H Suwadi

Khalid A Zainalabedin

Songhua Chen

Mark Anton Proett

Weijun Guo

Tarek M Habashy

Martin E Poitzsch

James A Grau

Alberto F Marsala

Weichang Li

Michael Wilt

Peng Lu

Sharath Chandra Mahavadi

Fei Le

Gong Li Wang

Klemens Katterbauer

Henry N Bachman

Raghu Ramamoorthy

Shawn David Taylor

Yupeng Li

Philip M Singer

Ronald E Plasek

Steve F Crary

Simone Di Santo

Dmitry Eskin

Ali Abdallah Alyousef

Bastian Sauerer

Mamdouh N Al-Nasser

Christopher Ayadiuno

Abdallah A Alshehri

Syed Shujath Ali

Benjamin Nicot

Gabriela Singer

Olanrewaju A Abudu

Mohammed Fadhel Al-Hamad

Farhan Ali

Ryan Antle

Noureddine Benlakhdar

Sherif Ghadiry

Mamdouh N Nasser

Mohamed Saif

Sachit Saumya

Nedhal Mohamed Abdullah Al-Musharfi

Ali Jasim A Al Solial

Pablo J Saldungaray

Maolin Luo

Ali R Belowi

Robert Wilson

Ahmed Al-Muthana

Nacer Guergueb

Saleh B Al-Ruwaili

Ramy Ahmed Mohamed

Aida Abdullah Abri

Abdrabrasool A Al-Hajari

Kamran B Husain

Robin Singh

Leilei Zhang

Nedhal Musharfi

Marie Van Steene

A method of analyzing a downhole fluid includes performing measurements of a dielectric property of the downhole fluid, the downhole fluid including a mixture of a filtrate fluid circulated through a borehole and a connate fluid. The measurements include a first measurement of the downhole fluid, and one or more additional measurements, each additional measurement performed after an amount of the filtrate fluid is injected into the downhole fluid to change the volume ratio, each additional measurement performed when the downhole fluid has a different volume ratio. The method also includes acquiring a plurality of reference profiles of respective reference mixtures, the respective reference mixtures including a first fluid and a reference fluid, comparing the measurements to each reference profile, and estimating a value of the dielectric property of the connate fluid based on the comparing.

Estimation of dielectric properties of a downhole fluid

A method includes obtaining, with an edge computing device disposed in a bottom hole assembly of a drilling system, logging while drilling data. The method further includes determining, with the edge computing device, at least one interpreted quantity of a subsurface using one or more artificial intelligence (AI) models processing the logging while drilling (LWD) data. The method further includes determining, with the edge computing device, an optimal set of data transfer parameters for a telemetry system governed by a set of data transfer parameters, to telemeter the at least one interpreted quantity, where the set of data transfer parameters are continually updated to the optimal set of data transfer parameters while the logging while drilling data is being acquired. The method further includes transmitting, with the edge computing device, one or more guidance control parameters to a geosteering system based on the at least one interpreted quantity.

Methods and systems for logging while drilling and optimized telemetry using artifical intelligence

A method for obtaining geological heterogeneity trends of a geological formation, including the steps: drilling wells (w, w, w, w, w) that penetrate the formation, acquiring well logs (log, log) for each well (w, w, w, w, w) as function of depth interVals (D) of the respectiVe well, determining a third degree tensor (T), where a z-dimension denotes the depths, a x-dimension denotes the well logs, and a y-dimension denotes the wells, extracting matrices (L, L, . . . , L) from the tensor (T), clustering the matrices (L, L, . . . , L) based on the characteristics of the corresponding well logs (log, log) to a clustering result matrix, aggregating the clustering result matrix to a cluster ensemble (π, π, . . . , π), and spatial partitioning the cluster ensemble (π, π, . . . , π) to a map that shows the geological heterogeneity trends associated with cluster types of the wells (w, w, w, w, w).

Method for obtaining geological heterogeneity trends of a geological formation

A method for constructing a high fidelity logging while drilling (LWD) log that includes obtaining a temporal well depth log and a plurality of temporal LWD logs from a drilling operation. The method further includes obtaining a temporal record of operational drilling parameters from the drilling operation. The method further includes determining, using a first machine-learning model, a temporal history of the drilling operation and constructing, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths. The method further includes processing the plurality of temporal property logs with, at least, a second machine-learning model to form a plurality of corrected temporal property logs and aggregating the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs.

Using artificial intelligence methods to exploit well logging data

Methods and systems are provided for predicting thermal properties of a subsurface rock formation. A training dataset is derived from petrophysical properties of a plurality of formation rock samples and thermal properties of the plurality of formation rock samples. The training dataset is used to train a machine learning model that predicts label data representing the predefined set of thermal properties given input data representing the predefined set of petrophysical properties of an arbitrary formation rock sample. The machine learning model can be validated and deployed for use in predicting thermal properties of subsurface rock formations.

Methods and systems for predicting formation thermal properties

Method and systems are provided that characterize a geological formation, which involve collecting or obtaining electrical signal data that results from interaction of electrical signals with the formation. The electrical signal data is processed to determine data characterizing relative permittivity and conductivity of the formation. The data characterizing relative permittivity and conductivity of the formation are used as inputs to models to determine data characterizing water saturation of the formation, particularly useful in formation evaluation and reservoir saturation dynamic monitoring in fresh water and mixed salinity environments.

Quantification of formation water saturation and salinity using relative permittivity and conductivity measurements

A fluid velocity sensor can include a thermally conductive body comprising a first temperature sensor, a second temperature sensor, and a third temperature sensor. A heating element is secured within the fluid velocity sensor to heat the thermally conductive body. The first temperature sensor, the second temperature sensor, and the third temperature sensor each reside within the thermally conductive body at different radial distances from the heating element. A flowrate of a fluid in contact with the fluid velocity sensor is determined based on a comparison between the internal temperatures measured by each of the first, second, and third temperature sensors.

Fluid flow velocity measurement via thermal conduction

A production logging tool includes a housing having a first end configured to connect to a well string disposed in a wellbore, a flowmeter assembly supported by the housing and including a spinner exposed to fluid in the wellbore, and a cleaning assembly connected to the housing and including a fluid nozzle directed at the spinner. The spinner rotates about a central rotational axis of the spinner, and the fluid nozzle direct a flow of cleaning fluid at the spinner. The cleaning assembly can include a fluid pump fluidly connected to the fluid nozzle to drive the flow of cleaning fluid to the fluid nozzle, and a controllable fluid valve between the fluid pump and the fluid nozzle to control the flow of cleaning fluid to the fluid nozzle.

Production logging spinner cleaning

An assembly and a method for cooling a downhole logging tool. The assembly includes a coiled tubing assembly having a downhole logging tool and a coiled tubing pipe. The coiled tubing pipe is coupled to the downhole logging tool. The coiled tubing pipe includes a cooling fluid tube. The cooling fluid tube conducts a cooling fluid to the downhole logging tool.

Cooling wellbore logging tools

Methods and systems are provided for characterizing connate water salinity and resistivity of a subsurface formation. Well log data including resistivity and spontaneous potential (SP) log data are measured by at least one downhole tool disposed within a borehole. The resistivity and SP log data are inverted to determine a resistivity model and an SP model, which are used to determine connate water resistivity. The connate water resistivity is used to determine connate water salinity. The connate water salinity derived from the inversion of resistivity log data and SP log data (or derived from a trained ML system supplied with such log data) can be used as a baseline measure of connate water salinity, and this baseline measure can be evaluated together with the connate water salinity estimates derived from pulsed neutron tool measurements over time-lapsed periods of production to monitor variation in connate water salinity due to production.

Methods for predicting and monitoring downhole salinity variations

A method can include receiving Stoneley wave data acquired by a sonic tool disposed in a borehole in a formation; performing an inversion using the Stoneley wave data to generate formation and borehole information; and identifying an interval along the borehole for performance of a downhole acquisition operation by a downhole tool using a machine learning model and the formation and borehole information as input to the machine learning model.

Borehole acquisition operation interval via Stoneley wave

A computer-implemented method includes: accessing a first database holding information encoding a set of labels that specify a condition of at least one of: a surface pipe, or an underground enclosure that runs at a plurality of depth locations; accessing a second database holding a plurality of inspection logs that record measurement data of the surface pipe or underground enclosure; based on, at least in part, the labeling information and the plurality of inspection logs, training a deep learning model configured to classify, into the set of labels, the condition of the surface pipe or underground enclosure when presented with the inspection logs; applying the deep learning model to one or more newly received inspection logs containing measurement data of a new surface pipe or a new underground enclosure; and subsequently classifying, into the set of labels, the condition of the new surface pipe or the new underground enclosure.

System and method using machine learning for automated casing and/or tubing inspection log interpretation

A determination of a continuous oil density log for a hydrocarbon reservoir accessible via a well drilling into the formation having the reservoir. The logging operations may be conducted in the well to generate a carbon-oxygen ratio (C/O) log, a water saturation log, and a porosity log. A continuous oil density log may be determined using the C/O log, the water saturation log, the porosity log, and carbon and oxygen density values. The continuous oil density log may be used in further characterization and development of the hydrocarbon reservoir.

Determination of continuous oil density log for reservoir characterization

A system for analysis of isolated and connected porosities of a porous formation using permittivity is disclosed. An electrical subsystem can provide electrical signals for one or more of the porous formation or a representation of the porous formation; and the system can determine one or more of a rate of permittivity change (RPC) or permittivity ratio (PR) from a first estimation model that relates permittivity measurements and frequencies that are associated with the electrical signals, so that the system can generate a second estimation model using one or more of the RPC or the PR, associated with the isolated and connected porosities, where the second estimation model can be used with a total porosity of the porous formation to estimate or predict an isolated porosity and a connected porosity of a production porous formation.

System and method for estimating porosity of porous formations using permittivity measurements

A method of removing wellbore water from a production wellbore includes determining a production flow rate. The method also includes determining a target production flow rate of the production fluid and comparing the production flow rate to a flow rate threshold associated with the target production flow rate. The method also includes determining, based on a result of comparing the production flow rate to the flow rate threshold, that the production flow rate satisfies the threshold. The method also includes activating a fluid moving device fluidly coupled to a wellbore string extending from a terranean surface to the water to inject, into the water, at least one of i) a surfactant, ii) a gas, or iii) a foaming agent, allowing the water and the production fluid to emulsify together and thereby allowing a mixture of water and production fluid to flow to the terranean surface of the wellbore.

Removing wellbore water

Growing community of inventors

Dhahran, Saudi Arabia

Shouxiang Mark Ma