John Quirein

Use of multiattribute transforms to predict log properties from seismic data

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...

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

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. Actual target data from the training well is linked to the clusters. The linked clusters are analyzed for variance, etc. and fuzzy inference is used to select a portion of each cluster 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.

Variable Input Neural Network Ensembles in Generating Synthetic Well Logs

This paper discusses a hybrid method for construction of neural network ensembles (NNE) in generating synthetic well logs that is often driven by the needs of simulating unobtainable actual logs, reducing the operational cost, reconstruction of missing and/or bad log data, and minimizing the hazards associated with using radioactive sources. In this method, several computer-driven routines are developed to rank the candidate neural network inputs as a function of data partition, network complexity and initialization. Then a network pool is automatically formed having the selected candidate networks characterized with multi-set inputs and different hidden nodes. The ensemble optimization is performed using a multi-objective genetic algorithm by aggregating the ensemble validation error, complexity, and negative correlation into a single quantity of merit. The simulations applied to actual field examples demonstrate that using multi-set-input NNE is more robust than using single-set-input NNE with significantly reduced uncertainty and improved prediction accuracy on the new data for some applications.

Selecting optimum log measurements for hydraulic fracturing

We have developed a statistical method for investigating the importance of different log measurements for picking the best zones for hydraulic fracturing. We have determined the method’s applicability using data from unconventional reservoirs (Eagle Ford, Haynesville, Barnett, and a reservoir from the Middle East). The analysis began with single log measurements (e.g., gamma ray [GR], compressional and shear sonic [DTC and DTS], and spectral gamma ray [SGR], which could measure the radioactivity of uranium [U], potassium [K], and thorium [Th]). Other types of measurements, including density (RhoB), neutron porosity (NPHI), and resistivity, were added to obtain more complex logging suites. These log measurements were the inputs for this analysis. Each input combination was referred to as a “scenario.” Parameters such as effective porosity (PhiE), brittleness, total organic carbon (TOC), production index (PI), and fracture index (FI) were referred to as the outputs for the analysis. We have investigated linear and nonlinear combinations of the inputs to predict the outputs. Various scenarios, beginning with the simplest cases and ending with the most complete combination, were tested. The selection of log combinations was either based on the importance of individual logs or on industry-standard combinations (such as triple and quad combos). For each scenario, we computed correlation coefficients and root-mean-square errors of predicting the output parameters. The prediction accuracies generally increased as a result of increasing the number of input logs. Our analysis clearly found the importance of using SGR (for PI and FI prediction) and resistivity (for TOC prediction) logs. Based on comparison of the reconstruction results, actual values, and correlation coefficients/errors, we ranked the log combinations for predicting/modeling a specific parameter. The most challenging properties to model included TOC, PhiE, PI, and FI; the easiest properties to predict were brittleness and Young’s modulus.

Fast determination and characterization of formation resistivity anisotropy, dip, and fracture using multicomponent induction data

AbstractMulticomponent induction (MCI) logging measurements have been widely used in the past decade for determining formation resistivity anisotropy (horizontal and vertical resistivities: Rh and Rv), dip, and azimuth. Currently, almost all MCI processing and interpretation algorithms of determining Rh, Rv, dip, and azimuth are based on simplified transversely isotropic (TI) formation models. In most geologic environments, formations are layered or laminated, making the TI model a reasonable assumption. Subsurface formations usually contain different types of fractures (natural or drilling-induced), and exhibit azimuthal resistivity anisotropy in the bedding plane, which leads to formation biaxial anisotropy (BA) in the same bedding plane. (This type of media is usually called orthorhombic or orthotropic in mechanical engineering and geomechanics.) Therefore, MCI data processing based on TI models may not be valid in complex BA formations caused by fractures. MCI processing and interpretation methods bas...