Curtis A. Link

Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction

Neural‐network prediction of well‐log data using seismic attributes is an important reservoir characterization technique because it allows extrapolation of log properties throughout a seismic volume. The strength of neural‐networks in the area of pattern recognition is key in its success for delineating the complex nonlinear relationship between seismic attributes and log properties. We have found that good neural‐network generalization of well‐log properties can be accomplished using a small number of seismic attributes. This study presents a new method for seismic attribute selection using a genetic‐algorithm approach. The genetic algorithm attribute selection uses neural‐network training results to choose the optimal number and type of seismic attributes for porosity prediction.We apply the genetic‐algorithm attribute‐selection method to the C38 reservoir in the Stratton field 3D seismic data set. Eleven wells with porosity logs are used to train a neural network using genetic‐algorithm selected‐attrib...

AVO as a fluid indicator: A physical modeling study

Information on time-lapse changes in seismic amplitude variation with offset (AVO) from a reservoir can be used to optimize production. We designed a scaled physical model experiment to study the AVO response of mixtures of brine, oil, and carbon dioxide at pressures of 0, 1.03, and 2.07 MPa . The small changes in density and velocity for each fluid because of increasing pressure were not detectable and were assumed to lie within the error of the experiment. However, AVO analysis was able to detect changes in the elastic properties between fluids that contained oil and those that did not. When the AVO response was plotted in the AVO intercept-gradient domain, fluids containing oil were clearly separated from fluids not containing oil. This was observed in the AVO response from both the top and base of the fluids in the physical model. We then compared the measured AVO response with the theoretical AVO response given by the Zoeppritz equations. The measured and theoretical AVO intercept responses for the t...

Blind Test of Methods for Obtaining 2-D Near-Surface Seismic Velocity Models from First-Arrival Traveltimes

ABSTRACT Seismic refraction methods are used in environmental and engineering studies to image the shallow subsurface. We present a blind test of inversion and tomographic refraction analysis methods using a synthetic first-arrival-time dataset that was made available to the community in 2010. The data are realistic in terms of the near-surface velocity model, shot-receiver geometry and the datas frequency and added noise. Fourteen estimated models were determined by ten participants using eight different inversion algorithms, with the true model unknown to the participants until it was revealed at a session at the 2011 SAGEEP meeting. The estimated models are generally consistent in terms of their large-scale features, demonstrating the robustness of refraction data inversion in general, and the eight inversion algorithms in particular. When compared to the true model, all of the estimated models contain a smooth expression of its two main features: a large offset in the bedrock and the top of a steeply...

A Comparison of the Finite-Element Method and Analytical Method for Modeling Unexploded Ordnance Using Magnetometry

Unexploded ordnance (UXO) is military ordnance that was fired, dropped, or emplaced but failed to function as intended and thus constitutes an explosive hazard. UXO is a worldwide problem that kills or maims thousands of civilians each year. Magnetic surveys are an efficient means of locating UXO containing ferrous metal when geologic conditions are sufficiently free of magnetic soil and rock. However, discrimination of UXO from non-UXO is complicated by the fact that UXO is often associated with high levels of clutter from ordnance fragmentation. To date, magnetic modeling of UXO has been based on calculations for a simple body of revolution geometry (prolate spheroids). We conducted an investigation to show how numerical modeling, in particular, finite-element modeling of more realistic geometries, compares to prolate spheroid results. Our results show that the calculated dipole moment response for complex models resembling actual UXO is up to 50% higher than the dipole moments for the prolate spheroid model. We also found that altering the shape of a model from a prolate spheroid to a complex shape has a greater effect on dipole moment than maintaining the same shape and altering the volume. Finally, in comparing the surface response from our models to real total field magnetic data, we find that complex models more closely match actual field data than prolate spheroid models. We suggest that modeling and, ultimately, discrimination using more realistic UXO shapes could result in significant improvements in distinguishing UXO from magnetic clutter and geology.

Crosswell imaging in a shallow unconsolidated reservoir

It has long been recognized by the oil industry that its future will rely more and more on the recovery of products from existing fields. The need for more efficient recovery drives the desire to know more about the reservoir structure and composition. Crosswell tomography is among the most promising of our geophysical utensils for reservoir characterization, as evidenced by its ever‐increasing application in EOR monitoring. Although it is a relatively new method, the literature shows that crosswell tomography works quite well in highly consolidated lithology where good data quality, in terms of signal‐to‐noise ratio and raypath coverage, is achievable. The case to be addressed here, however, is the one that occurs under more hostile conditions. Our intention is to show the ability of crosswell traveltime tomography in circumstances where data quality is lower than usual.

Land Streamer Aided Geophysical Studies at Saqqara, Egypt

During December 2002 and January 2003, Montana Tech in collaboration with Ain Shams University, Cairo, collected Ground Penetrating Radar (GPR) and seismic data at Saqqara, Egypt. The purpose of this study was to see if GPR and seismic methods could detect manmade structures in the subsurface at Saqqara. In particular, land streamer aided, seismic diving-wave tomography was tested as a method to detect archaeological features. Saqqara was one of the principal necropolises of Memphis, an ancient capital of Egypt. The research site was near the 3rd Dynasty pharaoh Djoser’s Step Pyramid—the first monumental structure built entirely of stone. A preliminary GPR study of our site yielded numerous, possibly manmade features in the subsurface with a 4m depth of penetration using 100MHz antennas. A follow-up three-dimensional (3-D) GPR survey over one of the more interesting features showed a broad trench underneath the flat-lying sand that is seen at the surface. This feature is most likely manmade because the ho...

Interpretation of Shallow Stratigraphic Facies Using a Self-Organizing Neural Network

A study was recently conducted to assess the extent of hydrocarbon impacts to groundwater and soil resources at a regional petroleum refinery. To accomplish the study, 46 groundwater-monitoring wells were installed at the site. Data collected from the wells included detailed lithologic descriptions from samples and cuttings, and suites of geophysical well logs. Because the quality of the lithologic descriptions was erratic, our approach was to produce lithofacies interpretations based on gamma ray logs, used as input to a neural network classifier system.

Oil Reservoir Porosity Prediction Using a Neural Network Ensemble Approach

The problem of parameter prediction in a hydrocarbon reservoir is typically accomplished by an interpreter using sparse well information and seismic data. The resulting maps may contain varying levels of uncertainty depending on the experience of the interpreter and the availability and quality of seismic and well data.

Crosshole tomography in the Seventy‐Six West field

The Seventy‐Six West field is located in the northwest corner of Duval County, south Texas. The field covers sections 61, 62, 63, 64, 80, 81, and 86 and is located some four miles west‐northwest of Freer, Texas. Production is mainly from the Jackson‐Yegua sands at a depth of about 1350 ft. Average daily production for the field is about 200 b/d.

Learning Machine Identification of Ferromagnetic UXO Using Magnetometry

The fundamental problem in applying geophysical mapping to locate unexploded ordnance (UXO) is distinguishing true UXO from non-UXO. Enhancing the accuracy of UXO detection has multiple benefits, especially in the areas of cost savings and safety. We investigated discrimination approaches using both magnetic field data and numerically modeled data. Libraries of total field magnetic (TFM) responses were calculated using finite element modeling for three UXO types found at a Montana National Guard training site. UXO model parameters were varied over ranges of azimuth, declination, and depth resulting in approximately 600 models per UXO type. The modeled responses of finite-element model (FEM) and actual TFM field data were then used as training data in discrimination and classification approaches comparing neural networks (NN), random forests (RF), and support vector machines (SVMs). The prediction targets in the training process comprised three classes: 1) binary [UXO or noninteresting object (NIO)]; 2) multiclass (UXO round type and NIO); and 3) classes derived from multiclass self-organizing feature map (SOFM) analysis. The multiclass SOFM targets generated from site-specific field data were found to be optimal for UXO discrimination. The best performing combination of class selection types using recentered data for UXO detection rates of 100% resulted in a false alarm rate (FAR) of 28%.