Saudi Arabian Oil Company

Mokhles Mustapha Mezghani

Fatai A Anifowose

Mustafa Ali H Al Ibrahim

Mustafa A Al Ibrahim

Larry S K Fung

Abdul Hafiz O Masri

Marwah Al Ismail

Bodong Li

Abdul Hafiz Masri

Leyla Ismailova

Salem Hamoud Shammari

Ming Lei Zhang

Sedat Inan

Mahdi A AbuAli

Nazih F Najjar

Vladislav Torlov

Sergey Safonov

Rainer Zuhlke

Johannes Jacobus Buiting

Mahdi Abuali

Larry S Fung

Hussain Ali Alqattan

Christian Andry Cesari

Walid K Hussein

Jean-Francois Mengual

Conrad K Allen

Abdullah Qasem

Egor Tirikov

Saleh M Badawood

Javed Ismail

Mustafa Al-Ibrahim

Assad Hadi Ghazwani

Mohammed Ali Al-Masrahy

Larry SK Fung

Johannes Jacobus Maria Buiting

A method and a system for estimating a thermal maturity of a rock sample of a subterranean region of interest are disclosed. The method includes preparing a plurality of rock samples of the subterranean region of interest and obtaining an image of an organic matter sample from the plurality of the rock. Further, the histograms are obtained based on RGB pixel values extracted from the image of the organic matter sample and a functional relationship describing the histograms is determined. Additionally, the method includes constructing a regression model using weight values of the functional relationship as input values and estimating the thermal maturity of the rock sample of the subterranean region of interest based on the constructed regression model.

Automated thermal maturation estimation from palynological sample images

A modular system for analyzing drilled cuttings includes a sampler unit, a washer unit, an analysis unit, and a central processing unit. The sampler unit receives the drilled cuttings from a shale shaker disposed on a rig site that obtains the drilled cuttings. The washer unit removes debris from the drilled cuttings. The analysis unit determines lithological properties of the drilled cuttings. The packager unit packages the drilled cuttings. The central processing unit coordinates operations to process the drilled cuttings through each of the sampler unit, washer unit, analysis unit and packager unit. The central processing unit facilitates a processing link among the sampler unit, washer unit, analysis unit and packager unit so that the sampler unit, washer unit, analysis unit and packager unit are integrated to form the modular system.

System and method for automated drill cutting sampling, preparation, analysis, and packaging

A modular geologic core examination table is claimed. The modular geologic core examination table includes at least two legs, each leg comprising an extending portion for increasing the effective length of each leg, the extending portion at least partially concentrically disposed within or around the leg from which it extends, and wheels affixed to each of the at least two legs, wherein the wheels comprise braking functionality. The modular geologic core examination table also includes a tabletop coupled to the at least two legs, the tabletop including a top surface for receiving a core sample tray configured to hold the geologic core samples, wherein the tabletop is disposed above an automated guided vehicle (AGV) configured to navigate the modular core examination table while carrying geologic core samples from one geologic processing station to another without collisions.

Robotic modular geologic core preparation and examination table

A method for generating a core description is disclosed. The method includes coring and collecting rock cores from geographical locations in the subterranean formation, detecting, using an augmented reality (AR) device worn by a user, content of an identifying tag of a rock core within a device view of the AR device to identify a well where the rock core is obtained, retrieving, by the AR device from a data repository, historical data of the well, activating, by the AR device, a sensor to acquire additional data from the rock core to supplement the historical data, and presenting, by the AR device, an AR image including a first image of the historical data and the additional data superimposed over a second image of the rock core, where the user generates the core description based on viewing the AR image.

Interactive core description assistant using virtual reality

A system and method automatically orient geological core samples. The system comprises a camera, a core analyzer module, and a core sample rotation apparatus. The camera is configured to capture a reference image of a cross-section of a reference core sample from a well and to capture a sample image of a cross-section of the to-be-oriented core sample from the well. The core analyzer module comprises code executable in a hardware processor to process the sample image to substantially match the reference image, and to determine an orientation angle of the match. The core sample rotation apparatus is configured to orient the to-be-oriented core sample by the orientation angle. A method comprises steps performed during operation of the system.

System and method for orienting geological core samples

A system and method for determining a noise-attenuated wellbore image is disclosed. The method includes obtaining a plurality of training images of a first wellbore wall portion, where each training image includes a first signal component and a first noise component, and training, using the plurality of training images, an artificial neural network to estimate the first signal component of one of the plurality of training images. The method further includes obtaining an application image of a second wellbore wall portion, including a second signal component and a second noise component, and determining the noise-attenuated wellbore image by applying the trained artificial neural network to the application image, wherein the noise-attenuated wellbore image comprises the second signal component.

Method for denoising wellbore image logs utilizing neural networks

A method including obtaining, by a computer processor, at least one key log in each of a set of training wells located, at least partially, within a hydrocarbon reservoir, identifying a target formation bounding surface in each of the set of training wells, and generating an initial depth surface for the target formation bounding surface from the target formation bounding surface in each of the set of training wells. The method further including, determining from the initial depth surface an initial depth estimate of the target formation bounding surface at a location of a current well, forming an objective function based, at least in part on a correlation between each key log in each of the set of training wells, and each corresponding key log in the current well, and optimizing the objective function by varying a depth shift between each of the set of training wells and the current well, to determine an optimum depth shift that produces an extremum of the objective function. The method still further including combining the initial depth estimate of the target formation bounding surface at the location of the current well with the optimum depth shift to produce a final depth estimate of the target formation bounding surface at the location of the current well.

Method to automatically pick formation tops using optimization algorithm

A geological core inspection system that includes a table to support core samples for inspection, a robotic geological core inspection system including a core sample sensing system to acquire sample inspection data (including an imaging sensor and a core sample position sensor), a core sample interaction system (including a dispensing system and a scoring system), and a robotic positioning system, and a control and communications system to provide for remote control of the core sample sensing system. The system further including a remote geological core inspection system to receive and communicate remote commands specifying requested operations of the robotic geological core inspection system (the control and communications system adapted to control operation of the core sample sensing system in response to the remote commands to perform the requested operations) and receive and present core data.

Geological core laboratory systems and methods

A method for determining a real-time pore pressure log of a well in a reservoir, including the steps: storing existing data logs of surface drilling parameters, logging while drilling (LWD), and mud gas of existing wells in a database, storing existing pore pressure logs of the existing wells in the database, wherein the existing pore pressure logs correspond to the existing data logs, determining a relationship between the existing data logs and the existing pore pressure logs, drilling a new well into the reservoir, determining new data logs of surface drilling parameters, LWD, and mud gas of the new well while drilling the new well, inputting the new data logs of the new well into the relationship while drilling the new well, determining a real-time pore pressure log of the new well by outputting an estimated pore pressure at a certain depth by the relationship while drilling the new well.

Method for determining pore pressures of a reservoir

A method for formation properties prediction in near-real time may include obtaining lab measurements of existing drill cuttings at a plurality of depths of a first well. The method may include obtaining historical drilling surface data at the plurality of depths from a plurality of wells. The method may include obtaining real-time digital photos and real-time drilling surface data of new drill cuttings at a new depth of a new well. The method may include generating, using a prediction model, predicted formation properties of the new drill cuttings based on the real-time digital photos, the real-time drilling surface data, and the new depth. The method may include predicting, using a near-real-time model and the predicted formation properties, near-real-time formation properties in the new well, wherein the prediction model comprises a historical model that employs a machine-learning algorithm.

System and method for formation properties prediction in near-real time

An example system may be used to automate one or more processes relating to drill cutting handling, cleaning, and packing to produce consistently high-quality cleaned samples. In some implementations, an example system may employ cleaning processes including centrifugal and ultrasonic processes to clean a sample automatically without the aid of a human operator thereby increasing efficiency and quality of the produced samples for better subsequent sample analysis. A system may include an unloading module, a cleaning module, and packing module, all of which may be operated in combination with a robotic arm.

Systems and methods for processing drill cuttings

A method for formation pore pressure prediction involves obtaining an input parameter set while drilling a well. The input parameter set includes surface drilling parameters, logging while drilling parameters, and advanced mud gas measurements. The method further involves generating, from the input parameter set, a reduced input parameter set, by eliminating at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure, and predicting the pore pressure by applying a machine learning model to the reduced input parameter set.

Method and system for formation pore pressure prediction with automatic parameter reduction

Systems and methods include a method for generating a real-time permeability log. Historical mud gas-permeability data is received from previously-drilled and logged wells. The historical mud gas-permeability data identifies relationships between gas measurements obtained during drilling and permeability determined after drilling. A formation hydrocarbon mobility model is trained using machine learning and the historical mud gas data. Real-time gas measurements are obtained during drilling of a new well. A real-time permeability log is generated for the new well using the formation hydrocarbon mobility model and real-time gas measurements.

Real-time estimation of formation hydrocarbon mobility from mud gas data

A method for determining a property of a formation, including the steps: drilling a well in the formation, collecting drill cuttings from the well, taking a digital image of each drill cutting, entering each digital image to a trained first model that outputs a predicted lithology class of each drill cutting from each digital image, taking a random number of X-ray diffraction (XRD) images of the drill cuttings, while at least one XRD image is selected from each lithology class, entering each XRD image and the corresponding digital image into a trained second model that predicts a property of the drill cuttings, and determining the property of the formation by determining the properties of the drill cuttings as a function of the depth of the drill cuttings.

Method for determining properties of a formation

A method of determining the depth of a sidewall core sample taken from a borehole relative to a reference log of the borehole. The method includes obtaining a reference log recorded on a reference log depth scale and a borehole image log recorded on a borehole image log depth scale of a portion of the borehole from which the sidewall core sample has been taken. The method further includes generating a calibrated borehole image log from the borehole image log and the reference log and identifying a candidate sidewall core image artifact in the calibrated borehole image log. The method also includes assigning a confidence value for the candidate sidewall core image artifact based on a characteristic of the candidate sidewall core image artifact, and determining, using the confidence value, a probability that the sidewall core sample was collected at a certain depth on the reference log depth scale.

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Dhahran, Saudi Arabia

Mokhles Mustapha Mezghani