Saudi Arabian Oil Company

Sergey Safonov

Muhammad Arsalan

Anton Gryzlov

Mokhles Mustapha Mezghani

Arturo Magana-Mora

Mustafa A Al Ibrahim

Leyla Ismailova

Egor Tirikov

Dmitry Kovalev

Muqbil Alkhalaf

A method for monitoring solids content during drilling operations may include collecting real-time cuttings image data at a surface outlet of a natural resource well, determining cuttings characteristics data based on the real-time cuttings image data, collecting real-time surface mud data, and determining real-time, one-dimensional downhole cuttings information based on a multi-dimensional computational fluid dynamics model. The cuttings characteristics data may include cuttings size distribution, cuttings volume, cuttings velocity, cuttings orientation, cuttings area, or combinations thereof. The real-time surface mud data may include inlet mud parameters, drilling operational parameters, well planning parameters, or combinations thereof. Determining real-time, one-dimensional downhole cuttings information may include converting the multi-dimensional computational fluid dynamics model into a one-dimensional continuous cuttings transport model and computing an integrated one-dimensional continuous cuttings transport model. Inputs to the integrated one-dimensional continuous cuttings transport model may include the cuttings characteristics data and the real-time surface mud data.

Methods for monitoring solids content during drilling operations

A method may include obtaining, from various sensors, acquired sensor data regarding various multiphase flows in a multiphase flow meter that are sampled at a predetermined sampling frequency. The acquired sensor data may describe various transient signals that correspond to various gas droplets. The method may further include generating, based on the acquired sensor data, a flow model for the multiphase flow meter. The method may further include updating the flow model to produce a first updated flow model using simulated flow data. The method may further include updating the first updated flow model to produce a second updated flow model using simulated sensor data. The second updated flow model may be used to determine one or more flow rates within a multiphase flow.

Method and system for multiphase flow meter using updated flow model based on simulated data

A system includes a pipe, a multiphase fluid, and a measurement unit. The multiphase fluid is disposed within the pipe. The measurement unit is connected to the pipe and includes a cylindrical structure, a first magnet, a second magnet, a first coil, and a second coil. The cylindrical structure is submersed in the multiphase fluid and has a first end and a second end. The first magnet is connected to the first end of the cylindrical structure, and the second magnet is connected to the second end of the cylindrical structure. The first coil is wound around the outer circumferential surface of the pipe in a location corresponding to a location of the first magnet disposed within the orifice. The second coil is wound around the outer circumferential surface of the pipe in a location corresponding to a location of the second magnet disposed within the orifice.

Multiphase flow meter

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.

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Moscow, Russia

Sergey Safonov