A PONDED BASIN FLOOR FAN OUTCROP ANALOGUE: BUNKERS SANDSTONE, NORTHERN FLINDERS RANGES, AUSTRALIA

2003 ◽  
Vol 43 (1) ◽  
pp. 537 ◽  
Author(s):  
M.R.W. Reilly ◽  
S.C. Lang
Keyword(s):  
Gamma Ray ◽  
South Australia ◽  
Salt Diapir ◽  
Log Analysis ◽  
Data Set ◽  
Flinders Ranges ◽  
Active Salt ◽  
Basin Floor ◽  
High Degree ◽  
Gamma Ray Log

The Donkey Bore Syncline in the Northern Flinders Ranges of South Australia contains a generally finegrained deepwater succession of Early Cambrian age (Bunkers Sandstone) that outcrops on three sides of a syncline and flanks an active salt diapir to the east (Wirrealpa Diapir). Within the succession lies a basal sand-prone interval interpreted as a basin floor fan (BFF) ponded within a mini-basin on a topographically complex slope.The BFF comprises over 30 m of section with deposits that are dominantly massive clean sandstone beds (0.1– 3 m thick) that are stacked or interbedded with siltstones and pinch out along strike.Eight stratigraphic sections and accompanying spectral gamma ray logs (using a hand held scintillometer) were measured through the BFF. Using spectral gamma ray log analysis combined with stratigraphic logs, four alternative correlation panels were constructed.Quantitative analysis of sand-prone intervals interpreted in each of the panels provided data on the vertical and horizontal connectivity within the BFF as different correlation methods were explored and the geological model improved. Quantitative analysis of vertical and horizontal connectivity values indicates a high degree of heterogeneity within the BFF, with poor–moderate vertical connectivity, with individual beds rarely correlating >500 m along strike. This heterogeneity is poorly resolved using conventional wireline log suites, but is greatly improved if spectral gamma ray logs are used (especially Thorium).The data set provides a high-resolution analogue for understanding the internal architecture of deepwater hydrocarbon reservoirs.

The transition from salt diapir to weld and thrust: examples from the Northern Flinders Ranges in South Australia

2021 ◽  
Author(s):  
Oskar Vidal‐Royo ◽  
Mark G. Rowan ◽  
Oriol Ferrer ◽  
Mark P. Fischer ◽  
J. Carl Fiduk ◽  
...  
Keyword(s):  
South Australia ◽  
Salt Diapir ◽  
Flinders Ranges

MACHINE LEARNING FOR PRODUCTIVITY PREDICTION IN HETEROGENEOUS CARBONATE GAS RESERVOIRS, CENTRAL SICHUAN BASIN, CHINA

2021 ◽  
Author(s):  
Zuoan Zhao ◽  
◽  
Dali Wang ◽  
Keyword(s):  
Machine Learning ◽  
Gamma Ray ◽  
Pore Space ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Production Rates ◽  
Gas Reservoirs ◽  
Data Set ◽  
Productivity Prediction ◽  
Gamma Ray Log

An approach of machine learning was used to evaluate and predict the production of the heterogeneous carbonate gas reservoirs in the horizontal development wells of the late Precambrian Dengying Formation. The present data set of machine learning consists of gamma ray log, laterolog, high-resolution electrical image logs, and production rate data. The previous data set acquired the conventional openhole logs, including gamma ray log, neutron-density log, sonic log, laterolog, and dipole acoustic log. The challenge in the previous data set was that the training process for machine learning was not convergent. It was most likely that the conventional log responses did not fully correspond to the productivity of the heterogenous carbonate gas reservoirs. Forty-one wells associated with the present data set were used to set up the training sample data set for the machine learning to the productivity prediction of the carbonate gas reservoirs. The data set construction includes log depth shift, calibrated image log creation, classification of reservoir types from core and carbonate reservoir heterogeneity variables extraction from image logs. Core observation and core laboratory analysis indicate that the pore space of the carbonate gas reservoirs mainly consists of vugs, caves, and fractures. However, the vugs and caves are selectively developed and randomly distributed both laterally and vertically. This represents a complex heterogeneous carbonate reservoir in which the vugs and caves are key contributor to the total pore space of the carbonate gas reservoir. The attributes of the vugs and caves can be extracted from the electrical image logs, including connectedness, surface proportion, size, and thickness of vug, and cave zones. Six horizontal development wells were used to validate the machine learning approach. The predicted gas production rates in the four wells separately were 700,000 m3/d, 2,000,000 m3/d, 800,000 m3/d, 300,000 m3/d, 1,100,000 m3/d and 1,180,000 m3/d, and the respective actual gas production rates are 1,019,790 m3/d, 1,820,000 m3/d, 800,000 m3/d, 396,000 m3/d , 1,700,000 m3/d, and 1,411,900 m3/d. The machine learning workflow and approach provided satisfactory results in the six horizontal wells. Subsequently, the electrical image logs have run in the standard logging program in the more than 50 horizontal development wells.

Development of banded terrain in an active salt diapir: potential analog to Mars

Geomorphology ◽  
2021 ◽  
pp. 107824
Author(s):  
Amos Frumkin ◽  
Shachak Pe’eri ◽  
Israel Zak
Keyword(s):  
Salt Diapir ◽  
Active Salt

Bayesian deconvolution of gamma‐ray logs

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1535-1546 ◽  
Author(s):  
Ping Sheng ◽  
Benjamin White ◽  
Balan Nair ◽  
Sandra Kerford
Keyword(s):  
High Frequency ◽  
Gamma Ray ◽  
Simulated Data ◽  
Field Measurements ◽  
Bayesian Optimization ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Data Application ◽  
Gamma Ray Detector ◽  
Gamma Ray Log

The spatial resolution of gamma‐ray logs is defined by the length 𝓁 of the gamma‐ray detector. To resolve thin beds whose thickness is less than 𝓁, it is generally desirable to deconvolve the data to reduce the averaging effect of the detector. However, inherent in the deconvolution operation is an amplification of high‐frequency noise, which can be a detriment to the intended goal of increased resolution. We propose a Bayesian statistical approach to gamma‐ray log deconvolution which is based on optimization of a probability function which takes into account the statistics of gamma‐ray log measurements as well as the empirical information derived from the data. Application of this method to simulated data and to field measurements shows that it is effective in suppressing high‐frequency noise encountered in the deconvolution of gamma‐ray logs. In particular, a comparison with the least‐squares deconvolution approach indicates that the incorporation of physical and statistical information in the Bayesian optimization process results in optimal filtering of the deconvolved results.

The Dawson Hill Member of the Grindstone Range Sandstone in the Flinders Ranges, South Australia

2010 ◽  
Vol 134 (1) ◽  
pp. 115-124 ◽  
Author(s):  
J.B. Jago ◽  
C.G. Gatehouse ◽  
C.McA. Powell ◽  
T. Casey ◽  
E.M. Alexander
Keyword(s):  
South Australia ◽  
Flinders Ranges
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