Exxonmobil Upstream Research Company

Exxonmobil Research and Engineering Company

Peng Xu

Limin Song

Robert J Pottorf

Enru Liu

Gordon Macleod

Dennis Patrick Smith

Narasimha-Rao Venkata Bangaru

Jeffrey Roberts Bailey

Krishnan Kumaran

Matthias Imhof

Lei Wang

Niranjan A Subrahmanya

Amr S El-Bakry

Vishwas Gupta

Erika A O Biediger

Jingbo Wang

Antonio R Paiva

Joseph K Bjerkseth

Weike Sun

Steven F Sowers

An example method can comprise creating a non-linear neural network based model of a system based on historical operational data of the system and receiving first sensor data from a plurality of sensors associated with the system. Predicted next sensor data can be determined based on the received first sensor data and the non-linear network model. Second sensor data can be received from the plurality of sensors, and a measure of deviation between the predicted next sensor data and the received second sensor data is calculated. In response to the measured deviation exceeding a predefined threshold; it can be determined that a fault has occurred.

Methods and systems for fault detection and identification

A diagnostic apparatus configured to communicate with a well test system comprising a plurality of wells in a field, comprising a receiving component configured to receive a well test data from the well test system, a transmitting component configured to transmit an abnormal well test signal indication, at least one processor configured to communicate with the transmitting component and the receiving component, and a memory coupled to the at least one processor, wherein the memory comprises instructions that when executed by the at least one processor cause the diagnostic apparatus to compare the well test data to one or more well test descriptors stored in memory, correlate the well test data to an abnormal well test result selected based at least in part on the comparison with the one or more well test descriptors stored in the memory, and instruct the transmitting component to transmit an abnormal well test signal indication to a recipient.

Automated well test validation

Systems and methods which determine geologic properties using acoustic analysis are shown. Acoustic signals are collected during processing (e.g., crushing, shearing, striking, compressing, etc.) of geologic media, such as rock samples, for determining geologic properties according to embodiments. The acoustic signals collected may include frequency information, amplitude information, time information, etc. which may be utilized in determining geologic properties, such as geologic media properties (e.g., mineralogy, porosity, permeability, sealing capacity, fracability, compressive strength, compressibility, Poisson's Ratio, Youngs Modulus, Bulk Modulus, Shear Modulus), geologic structure properties (e.g., lithology, seal quality, reservoir quality), geologic acoustic properties (e.g., acoustic logging effectiveness, acoustic response, natural or harmonic frequencies, etc.). Embodiments may be used to provide determination of geologic properties from a variety of geologic media samples, such as cuttings, core samples, etc.

Systems and methods for determining geologic properties using acoustic analysis

Methods and systems for controlling drilling operations include using a statistical model to identify at least two controllable drilling parameters having significant correlation to one or more drilling performance measurements. The methods and systems further generate operational recommendations for at least two controllable drilling parameters based at least in part on the statistical model. The operational recommendations are selected to optimize one or more drilling performance measurements.

Automatic drilling advisory system based on correlation model and windowed principal component analysis

Method for generating a new family of seismic attributes sensitive to seismic texture that can be used for classification and grouping of seismic data into seismically similar regions. A 2D or 3D data analysis window size is selected (), and for each of multiple positions () of the analysis window in the seismic data volume, the data within the window are transformed to a wavenumber domain spectrum (). At least one attribute of the seismic data is then defined based on one or more spectral properties, and the attribute is computed () for each window, generating a multidimensional spectral attribute data volume (). The attribute data volume can be used for inferring hydrocarbon potential, preferably after classifying the data volume cells based on the computed attribute, partitioning the cells into regions based on the classification, and prioritizing of the regions within a classification.

Method for seismic interpretation using seismic texture attributes

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Annandale, NJ, United States of America

Peng Xu