Geology topics

D. C. Kopaska-Merkel

Publications and source records attributed to D. C. Kopaska-Merkel.

4 recordsLinked to original sources

USGS2005

Development of the caudal exoskeleton of the pliomerid trilobite Hintzeia plicamarginis new species

The later juvenile ontogeny of the caudal plate of the early Ordovician pliomerid trilobite Hintzeia plicamarginis new species likely comprised an initial phase during which the rate of appearance of new segments subterminally exceeded that of segment release into the thorax, a short phase of constant segment numbers, and a later phase during which release occurred but in which no new segments appeared. A distinct terminal region became manifest in the second phase. During the second and third phases growth coefficients for individual segments were about 1.1-1.2 per instar. Although the shapes of segments varied during growth, the pattern of ontogenetic shape change appears to have been broadly similar among segments. This suggests an homonomous trunk segment morphology regardless of thoracic or caudal identity in maturity. These results imply that control of trunk exoskeletal segment appearance and articulation were decoupled in this trilobite, and that the terminal region had a distinct mature morphology. H. plicamarginis is described as a new species. ?? Blackwell Publishing, Inc.

Conference Paper↗

USGS2002

Identification of lithofacies using Kohonen self-organizing maps

Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

Computers & Geosciences↗

USGS2001

A lone biodetrital mound in the Chesterian (Carboniferous) of Alabama?

A carbonate mound in the Chesterian Bangor Limestone of Lawrence County, Alabama, consists chiefly of packstone and grainstone dominated by echinoderm ossicles and fragments of fenestrate bryozoans. In-situ colonies of the rugose coral Caninia flaccida comprise about 8% of the mound by volume. The exposed portion of the mound is approximately 25 m wide, 1.6 m thick at the thickest point and roughly circular in plan. The mound developed on top of a shallow ooid shoal that had been cemented and stabilised during an earlier episode of sub-aerial exposure. Subsequent flooding of the exposed shoal surface permitted establishment of the mound biota. Lateral and vertical facies relationships suggest that the mound possessed about 45 cm of synoptic relief when fully developed. Rugose corals, fenestrate and ramose bryozoans, stalked echinoderms, and sessile soft-bodied organisms encrusted by foraminifera colonised the shoal, forming a mound. Baffling resulted in deposition of mixed-fossil packstone containing locally derived debris and coated grains from the surrounding sea floor. Strong currents within the mound are indicated by preferred orientation of corals and by coarse, commonly cross-stratified grainstone in channels between neighboring coral colonies. Corals are most abundant on the windward side of the mound, where they account for about 13% of the mound compared to 6- 10% in the central part of the mound, and 2-4% on the leeward flank. Biodetrital mounds such as the one described here are uncommon in upper Paleozoic strata and previously unknown in the Bangor Limestone. Of 10 carbonate buildups we examined in the Bangor in Alabama and Tennessee, only one is a biodetrital mound. Two are rugose coral-microbial reefs, one is a coral biostrome, and six are dominated by microbialite. The Bangor shelf, previously interpreted as sedimentologically simple, appears to contain many small mounds of quite varied characteristics. Also, the discovery of a biodetrital mound in the Chesterian of Alabama suggests that there may be more kinds of upper Paleozoic mounds than commonly acknowledged. ?? 2001 Elsevier Science B.V. All rights reserved.

Sedimentary Geology↗

USGS2000

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Computers & Geosciences↗