A seismic attribute is a quantitative measure of a seismic characteristic of interest. Analysis of attributes has been integral to reflection seismic interpretation since the 1930s when geophysicists started to pick traveltimes to coherent reflections on seismic field records. There are now more than 50 distinct seismic attributes calculated from seismic data and applied to the interpretation of geologic structure, stratigraphy, and rock/pore fluid properties. The evolution of seismic attributes is closely linked to advances in computer technology. As examples, the advent of digital recording in the 1960s produced improved measurements of seismic amplitude and pointed out the correlation between hydrocarbon pore fluids and strong amplitudes (“bright spots”). The introduction of color printers in the early 1970s allowed color displays of reflection strength, frequency, phase, and interval velocity to be overlain routinely on black-and-white seismic records. Interpretation workstations in the 1980s provided...

Seismic attributes — A historical perspective

Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models

The present invention is directed to a method for inferring/estimating missing values in a data matrix d(q, r) having a plurality of rows and columns comprises the steps of: organizing the columns of the data matrix d(q, r) into affinity folders of columns with similar data profile, organizing the rows of the data matrix d(q, r) into affinity folders of rows with similar data profile, forming a graph Q of augmented rows and a graph R of augmented columns by similarity or correlation of common entries; and expanding the data matrix d(q, r) in terms of an orthogonal basis of a graph Q×R to infer/estimate the missing values in said data matrix d(q, r).on the diffusion geometry coordinates.

Methods for filtering data and filling in missing data using nonlinear inference

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including k-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although well documented in the literature, the terminology and complexity of these algorithms may bewilder the average seismic interpreter, and few papers have applied these competing methods to the same data volume. We have reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a tu...

A comparison of classification techniques for seismic facies recognition

Deep learning for seismic lithology prediction

We describe a new method for predicting well‐log properties from seismic data. The analysis data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs theoretically may be of any type; however, the greatest success to date has been in predicting porosity logs. From the 3-D seismic volume a series of sample‐based attributes is calculated. The objective is to derive a multiattribute transform, which is a linear or nonlinear transform between a subset of the attributes and the target log values. The selected subset is determined by a process of forward stepwise regression, which derives increasingly larger subsets of attributes. An extension of conventional crossplotting involves the use of a convolutional operator to resolve frequency differences between the target logs and the seismic data. In the linear mode, the transform consists of a series of weights derived by least‐squares minimization. In the nonlinear mode, a neural network is trained, using the selected att...

Use of multiattribute transforms to predict log properties from seismic data

Multiattribute seismic analysis is a broad term that encompasses all geostatistical methods that utilize more than one attribute to predict some physical property of the earth Bivariate geostatistics is obviously the simplest subset of the multivariate techniques and thus the standard technique of cokriging could be called multivariate geostatistics However, in this paper we will use the name to refer to geostatistical methods that use more than two variables Even then, there are many different methods that fall under this heading There are three major subcategories

Multiattribute seismic analysis

A system and method for selecting a training data set from a set of multidimensional geophysical input data samples for training a model to predict target data. The input data may be data sets produced by a pulsed neutron logging tool at multiple depth points in a cases well. Target data may be responses of an open hole logging tool. The input data is divided into clusters (16, 24). Actual target data from the training well is linked to the clusters. The linked clusters are analyzed for variance, etc. and fuzzy inference (34) is used to select a portion of each cluster (36) to include in a training set. The reduced set is used to train a model, such as an artificial neural network. The trained model may then be used to produce synthetic open hole logs in response to inputs of cased hole log data.

Neural network training data selection using memory reduced cluster analysis for field model development

To accurately locate microearthquakes that are genetically related to hydraulic fracture stimulation, a thorough knowledge of the velocity structure between monitoring and fracturing treatment wells is essential. Very fast simulated annealing (VFSA) is implemented to invert for a flat-layered velocity model between wells using perforation or string-shot data. A two-point ray-tracing method is used to find the ray parameter p for a ray traveling from a source to a receiver. The original traveltime-calculation formula is modified to account for the borehole source-receiver geometry. VFSA is used as a tool to optimize P- and S-wave velocities simultaneously. Unlike previous applications of VFSA, two improvements result from a new study: (1) both P- and S-wave arrival-time misfits are considered in a joint-objective function, and (2) P- and S-wave velocities are perturbed simultaneously during annealing. The inverted velocities follow the true values closely with a very small root-mean-square error, indicating the inverted model is close to the global minimum solution whose rms error should be zero for synthetic examples. Data noise contaminates inverted models, but not substantially in synthetic test results. A comparison of models inverted using VFSA and Occam's inversion technique indicates that inverted models using VFSA are superior to those using Occam's method in terms of velocity accuracy.

Velocity calibration for microseismic monitoring: A very fast simulated annealing (VFSA) approach for joint-objective optimization

Integrating Core Data and Wireline Geochemical Data for Formation Evaluation and Characterization of Shale-Gas Reservoirs

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