QUALITATIVE ANALYSIS OF SPECTRAL GAMMA RAY DATA AS A TOOL FOR FIELDWIDE AND REGIONAL STRATIGRAPHIC CORRELATION, ENDERBY TERRACE, CARNARVON BASIN, WESTERN AUSTRALIA

2001 ◽  
Vol 41 (1) ◽  
pp. 449
Author(s):  
A.P. Clare ◽  
A.J. Crowley
Keyword(s):  
Western Australia ◽  
Gamma Ray ◽  
Petroleum Industry ◽  
Stratigraphic Correlation ◽  
Environment Impact ◽  
Rock Types ◽  
Subsurface Environment ◽  
Detailed Assessment ◽  
Well Correlation ◽  
Carnarvon Basin

The use of wireline and core spectral gamma data as a tool for defining clay types and mineral assemblages in the subsurface environment has been widely used for many years within the petroleum industry. However, the qualitative use of radiometric data for interpreting rock types as used with airborne surveys in the mineral industry has not undergone detailed assessment as a well correlation tool.Applying the principles of qualitative airborne radiometric interpretation to the assessment of wireline spectral gamma ray data has proved extremely useful as a well correlation tool in the Carnarvon Basin of Western Australia. Data is presented from the Stag Field detailing the application of the technique as an effective fieldwide correlation tool. The sandstone reservoirs exhibit mineralogical variation and individual sand packages can be discontinuous. However, the major shale packages are laterally continuous and individual shales show remarkable character consistency over several kilometres. Such character continuity has proved a valuable correlation tool for confirming and refining the stratigraphic packages observed in the Cretaceous section of Stag.Success on the Stag Field led to application of the technique for regional correlations on the Enderby Terrace. The results of regional work show that correlations still hold when the technique is applied to correlations of over 70 km even though some lateral variation due to provenance and depositional environment impact on clay types was evident. Hence this qualitative approach of wireline log evaluation has proved an effective and valuable correlation tool.

Additional Early Permian ammonoid cephalopods from Western Australia

1990 ◽  
Vol 64 (3) ◽  
pp. 392-399 ◽  
Author(s):  
Brian F. Glenister ◽  
Cathy Baker ◽  
W. M. Furnish ◽  
G. A. Thomas
Keyword(s):  
Western Australia ◽  
Early Permian ◽  
West Texas ◽  
Permian Ammonoid ◽  
Carnarvon Basin

An ancestral paragastrioceratid, Svetlanoceras irwinense (Teichert and Glenister, 1952), and a specifically indeterminate gonioloboceratid, cf. Mescalites sp., from the basal Callytharra Formation are described as the oldest ammonoids recovered from the Permian of the Carnarvon Basin, Western Australia. Identity of these taxa strengthens correlation with the Holmwood Shale (Sakmarian) of the adjacent Perth Basin. Svetlanoceras moylei Mikesh, n. sp., from the Lenox Hills Formation of West Texas, is described for comparison with other simple paragastrioceratids.

Clay mineralogical, geochemical and isotopic tracing of the evolution of the Woodleigh impact structure, Southern Carnarvon Basin, Western Australia

2005 ◽  
Vol 149 (5) ◽  
pp. 576-590 ◽  
Author(s):  
I. Tonguç Uysal ◽  
Arthur J. Mory ◽  
Suzanne D. Golding ◽  
Robert Bolhar ◽  
Kenneth D. Collerson
Keyword(s):  
Western Australia ◽  
Impact Structure ◽  
Isotopic Tracing ◽  
Carnarvon Basin

Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks

2021 ◽  
Author(s):  
Aria Abubakar ◽  
Mandar Kulkarni ◽  
Anisha Kaul
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Gamma Ray ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Well Log ◽  
Machine Learning Model ◽  
Well Correlation ◽  
Geological Formations

Abstract In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.

DENSITY LOGGING

Geophysics ◽  
1960 ◽  
Vol 25 (4) ◽  
pp. 891-904 ◽  
Author(s):  
J. J. Pickell ◽  
J. G. Heacock
Keyword(s):  
Bulk Density ◽  
Gamma Ray ◽  
Petroleum Industry ◽  
Core Data ◽  
Response Characteristics ◽  
Calibration Curves ◽  
Ray Density ◽  
Log Interpretation

This review of density logging is primarily a compilation of information presented in the petroleum industry literature. It includes a brief discussion of some of the theory involved in gamma‐ray density logging, various calibration curves, comparisons of density‐log and core data, and comments on density‐log interpretation. Conclusions are that the density log, under good borehole conditions, provides an accurate means for measuring bulk density of the formation adjacent to the borehole. If grain density is known, valid estimates of porosity can also be made. Because of the response characteristics of the system, accuracy in determining porosity is best when formation densities are low and porosities are high.

Mimicking the process of manual sequence stratigraphy well correlation

2021 ◽  
pp. 1-65
Author(s):  
Huijing Fang ◽  
Yihuai Lou ◽  
Bo Zhang ◽  
Huaimin Xu ◽  
Man Lu
Keyword(s):  
Euclidean Distance ◽  
Well Logs ◽  
Stratigraphic Correlation ◽  
Time Warping ◽  
Error Matrix ◽  
Stratigraphic Model ◽  
Wireline Logs ◽  
Well Correlation ◽  
Dynamic Time ◽  
Error Computation

Stratigraphic correlation of well logs is based on interactive, interpreter-based pattern recognition. A skilled interpreter identifies similar patterns (such as upward fining and coarsening) in user-defined well sections and links them using either a conscious or subconscious stratigraphic model. This manual stratigraphic correlation of numerous wells in mature fields can be both time consuming and error prone. To expedite the process of stratigraphic correlation, we perform the semi-automatic stratigraphic correlation of wireline logs from multiple wells using the Improved Dynamic Time Warping (IDTW). The IDTW employs semblance, which compares the shape of the well logs, to replace the Euclidean distance in the pairwise error computation. The resulting error matrix is compatible with the lateral nonstationary variation of well logs in the same formation. The workflow begins with interpreting stratigraphic well tops on user-defined well sections that is similar to current process of stratigraphy analysis. The interpreted wells are then treated as reference wells to aid in interpreting well tops for other wells. Necessary manual interventions are incorporated during the process of the semi-automatic stratigraphic correlation. We applied the proposed method to two experimental fields: a sand-rich reservoir and a mud-rich reservoir. The applications illustrate that the proposed method performs well in aggradational strata and successfully predicts the discontinuities with manual interventions.

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