Abstract: This disclosure provide a bottom sediment determining device, which is inputted with an echo signal corresponding to an ultrasonic wave outputted underwater, and determines water bottom sediment using a neural network. The device includes a memory for storing two or more parameters to be used in the neural network so as to be associated with positional information, a receiver for receiving the positional information, an acquisition module for acquiring the parameters corresponding to the positional information, and a setting module for setting the parameters to the neural network.

Abstract: A method of estimating formation porosity using a neural network for neutron porosity tools. In the training stage, the near-to-far ratio, environmental variables, such as mineralogy, borehole size, standoff etc., are fed to the inputs and the neural network is trained for obtaining the related true porosity (the output). The trained neural network is implanted into tool's firmware for the real time porosity measurement, accounting for the environmental effects considered during training.

Abstract: Method, system, and computer program product for tracing seismic sections to convert to digital format are provided. The method includes: receiving a scan of a seismic section having multiple overlapping traces measured at spot points; splitting the traces into segments; associating the segments with appropriate baselines; estimating the traces between segments using logical rules; and deriving amplitudes of the traces.

Abstract: A system and method for modeling technology to predict accurately water-oil relative permeability uses a type of artificial neural network (ANN) known as a Generalized Regression Neural Network (GRNN) The ANN models of relative permeability are developed using experimental data from waterflood core test samples collected from carbonate reservoirs of Arabian oil fields Three groups of data sets are used for training, verification, and testing the ANN models Analysis of the results of the testing data set show excellent correlation with the experimental data of relative permeability, and error analyses show these ANN models outperform all published correlations

Abstract: A method of enhancing the safety of a region includes defining a set of rules for the region. Collecting field data from a plurality of mobile stations movable throughout the region. The field data includes information relating to the relative movement of the mobile stations. Comparing the collected field data to the set of rules to identify one or more areas of potential risk within the region.

Abstract: Methods, systems, and computer readable media are provided for fast updating of oil and gas field production optimization using physical and proxy simulators. A base model of a reservoir, well, or a pipeline network is established in one or more physical simulators. A decision management system is used to define uncertain parameters for matching with observed data. A proxy model is used to fit the uncertain parameters to outputs of the physical simulators, determine sensitivities of the uncertain parameters, and compute correlations between the uncertain parameters and output data from the physical simulators. Parameters for which the sensitivities are below a threshold are eliminated. The decision management system validates parameters which are output from the proxy model in the simulators. The validated parameters are used to make production decisions.

Abstract: An artificial neural network, ANN, and method of training the ANN for inversion of logging tool signals into well logs of formation parameters is disclosed. Properly selected synthetic models of earth formations are used to train the ANN. The models include Oklahoma and chirp type of formations. In each model parameter contrasts of from 10 to 1 to about 100 to 1 are included. Models including maximum and minimum parameter values spanning the operating range of the selected logging tool are included. Parameter contrasts at interfaces are limited to realistic values found in earth formations. The selected models are used to generate synthetic tool signals, which are then used as inputs to the ANN for training. When the ANN coefficients are properly adjusted to produce an output matching the original models, the ANN can be used for inversion of any real signals from the selected logging tool.

Abstract: It is possible to easily perform the spatial simulation for a large area. Provided is a computer-readable medium recorded with a spatial simulation program for controlling a computer to execute procedures for calculating a time-based change of a variable in a plurality of numerical grids to reproduce on a computer a time-based change of a phenomenon represented by the variable in a space represented by the plurality of numerical grids, the spatial simulation program including the procedures of: defining a sub domain composed of the plurality of numerical grids (201); setting a forcing condition for the sub domain (204); detecting the sub domain including the numerical grid in which the variable changes with time; detecting the sub domain including the numerical grid in which the variable referred to for calculating the time-based change is positioned (205); and updating variables in the numerical grids constituting the detected sub domain (208).

Abstract: A method for stimulation of hydrocarbon production via imbibition by utilization of surfactants. The method includes use of fuzzy logic and neural network architecture constructs to determine surfactant use.

Abstract: A method of predicting earthquakes includes the step of positioning a first transducer array adjacent to a seismically active region and below the water table, i.e., within the zone of saturation. The first transducer array includes a first plurality of seismometers, at least one first clock, and at least one first digitizer. The at least one first clock is in communication with at least one of the first plurality of seismometers, and the at least one first digitizer also is in communication with at least one of the first plurality of seismometers. The method also includes the steps of detecting a plurality of wave movements resulting from dilation of the crust of the Earth prior to an earthquake, and converting at least one of the wave movements into a first voltage. The method further includes the step of discriminating between wave movements resulting from dilation of the crust of the Earth and movements resulting from at least one other event.

Abstract: Methods are provided for downhole sensing and flow control utilizing neural networks. In a described embodiment, a temporary sensor is positioned downhole with a permanent sensor. Outputs of the temporary and permanent sensors are recorded as training data sets. A neural network is trained using the training data sets. When the temporary sensor is no longer present or no longer operational in the well, the neural network is capable of determining the temporary sensor's output in response to the input to the neural network of the permanent sensor's output.

Abstract: The invention relates to a method for automatic interpretation of geochemical measurements obtained by pyrolysis of a rock sample in order to obtain information pertaining to the organic matter contained in the sample. In this method, rock samples having known petroleum characteristics are used to carry out a phase of training of an artificial neural network, the neural network is used to obtain parameters pertaining to the organic matter of a rock sample, interpretation of the parameters is refined at the network output by using fuzzy sets for refining interpretation of the parameters at an output of the neural network.

Abstract: A method of determining characteristics of a rotary drag-type drill bit comprises the steps of: creating a computerized representation of the cutters on the drill bit, and then, with each cutter in turn, projecting the shape of the cutter onto a fixed plane having an array of cells, and assigning a first marker to those cells of the array which overlie the projection of the selected cutter. The representations of the other cutters are then rotated about the bit axis until they have all passed through the plane at least once, the cutters also being moved axially while being rotated so as to represent the axial movement of the bit during drilling. The shapes of the other cutters are projected onto the plane, as they pass through it, and a second marker is assigned to those cells of the array which overlie both the projection of the selected cutter and the projections of any of the other cutters.

Abstract: A neural network based system, method, and process for the automated delineation of spatially dependent objects is disclosed. The method is applicable to objects such as hydrocarbon accumulations, aeromagnetic profiles, astronomical clusters, weather clusters, objects from radar, sonar, seismic and infrared returns, etc. One of the novelties in the present invention is that the method can be utilized whether or not known data is available to provide traditional training sets. The output consists of a classification of the input data into clearly delineated accumulations, clusters, objects, etc. that have various types and properties. A preferred but non-exclusive application of the present invention is the automated delineation of hydrocarbon accumulations and sub-regions within the accumulations with various properties, in an oil and gas field, prior to the commencement of drilling operations.

Abstract: A wireless seismic survey system is structured as a hierarchy of cell network layers, each higher level cell network layer covering a bigger area than a lower level cell network layer and receiving data from the lower level cell network layer. This hierarchical structure functions to concentrate collected data to a level that can be communicated to a central collection point, either through wireless, cable or other media. By defining cells to cover predetermined areas, transmission frequencies and/or transmission time slots or codes can be reused by cells sufficiently spaced apart to avoid co-channel interference, thereby increasing the amount of usable bandwidth.

Abstract: A method for generating an improved estimate of horizontal conductivity, dip angle, azimuth and anisotropy parameter of an earth formation penetrated by a wellbore from dual-frequency transverse electromagnetic induction measurements, comprising generating an initial estimate of the horizontal conductivity, dip angle, azimuth and anisotropy parameter from the dual-frequency transverse induction measurements made at each one of a plurality of base frequencies. The initial estimates from each of the plurality of base frequencies are input into a primary trained neural network, and the improved estimate is calculated by the trained neural network. The network is trained by generating models of earth formations each having a known value of horizontal conductivity, anisotropy parameter, dip angle and azimuth. Voltages which would be measured by the transverse electromagnetic induction instrument in response to each model are synthesized.

Abstract: A method controlling development of an oil or gas reservoir uses a neural network and genetic algorithm program to define a neural network topology and the optimal inputs for that topology. The topology is defined from identified and selected (1) parameters associated with the formation or formations in which actual wells are drilled in the reservoir and (2) parameters associated with the drilling, completion and stimulation of those wells and (3) parameters associated with the oil or gas production from the wells. Subsequent drilling, completion and stimulation of the reservoir is determined and applied based on hypothetical alternatives input to the topology and resulting outputs.

Abstract: An automatic seismic pattern recognition method includes the steps of: determining a given number of seismic patterns to be recognized; providing a set of seismic trace portions for the region; defining a pattern recognition parameter common to all the trace portions, and determining the value of the parameters for each of the traces portions of the set. The method also includes the steps of: selecting trace portions of the set; selecting a one-dimensional neural network containing as many cells as there are patterns to be recognized where each cell is assigned a value of the recognition parameter; and submitting the neural network to a learning process with the selected trace portions so that at the end of the process each cell matches a pattern to be recognized and so that the patterns are progressively ordered.

Abstract: Methods of directly analyzing wireline well logging data to derive pore types, pore volumes and capillary pressure curves from the wireline logs are disclosed. A trained and validated neural network is applied to wireline log data on porosity, bulk density and shallow, medium and deep conductivity to derive synthetic pore type proportions as a function of depth. These synthetic data are then applied through a derived and validated capillary pressure curve data model to derive pore volume and pressure data as a function of borehole depth.