THE STOKES GAS FIELD, SOUTHWEST QUEENSLAND

1995 ◽  
Vol 35 (1) ◽  
pp. 12
Author(s):  
D.G. Bennett ◽  
R.S. Heath ◽  
S. Taylor
Keyword(s):  
Seismic Data ◽  
Complex Structure ◽  
South Australia ◽  
Reservoir Quality ◽  
Early Permian ◽  
Pressure Data ◽  
Gas Field ◽  
Flow Rates ◽  
South West ◽  
Water Saturated

The Stokes gas field is located in South West Queensland permit ATP 259P, close to the South Australia/Queensland state border. It was discovered and successfully appraised by the Stokes-1,-2 and -3 wells drilled during 1993 and early 1994. Productive zones, with DST flow rates of up to 237 x 103m3/d, are present in the Early Permian Epsilon and Patchawarra formations with moderate gas liquids contents present in the higher reservoirs. A total net pay thickness of 63 m occurs in Stokes-2. Generally, reservoir quality is moderate to good with core permeabilities occasionally exceeding one darcy. Some low deliverability Patchawarra Formation reservoirs are present which contain greater than 20 per cent kaolin. These microporous reservoirs are characterised by low resistivity responses similar to that of water saturated reservoirs.The field's discovery coincided with the onset of renewed South West Queensland gas exploration. Seismic data were recorded in 1990 and 1992 to mature the Stokes prospect to drillable status. The structure had been recognised as being highly prospective due to its regional setting. Proved and probable gas-in-place exceeds 5.7 x 109 m3 which approximates the highside case estimated from pre-drill probabilistic reserves distributions.Comprehensive reservoir pressure data were obtained from each well and were instrumental in locating appraisal wells and demonstrating that reservoirs are filled to the structural spill point. The Stokes-3 results indicate that some fault compartmentalisation may occur suggesting a more complex structure than originally mapped. Isolation of other reservoirs may also occur between Stokes-1 and -2.

Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Allocation of fracturing intervals and justification of fracture parameters in the pre-Jurassic deposits

2021 ◽  
pp. 9-19
Author(s):  
P. A. Boronin ◽  
N. V. Gilmanova ◽  
N. Yu. Moskalenko
Keyword(s):  
Permeability Coefficient ◽  
Complex Structure ◽  
High Flow ◽  
Well Testing ◽  
Rock Matrix ◽  
Unconventional Reservoir ◽  
Flow Rates ◽  
Matrix Permeability ◽  
The Matrix ◽  
Standard Set

The object of research in this article is the productive deposits of the pre-Jurassic complex. The pre-Jurassic complex is of great interest, this is an unconventional reservoir with a complex structure and developed fractured zones. High flow rates cannot be determined by the rock matrix, since the matrix permeability coefficient is on average 2−3 md. In this regard, there is the problem of separation of fractured intervals according to a standard set of well testing.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

Residency and movement dynamics of southern rock lobster (Jasus edwardsii) after a translocation event

2015 ◽  
Vol 66 (7) ◽  
pp. 623 ◽  
Author(s):  
Adrian Linnane ◽  
Shane Penny ◽  
Peter Hawthorne ◽  
Matthew Hoare
Keyword(s):  
Large Scale ◽  
South Australia ◽  
Rock Lobster ◽  
Directed Movement ◽  
Translocation Event ◽  
South West ◽  
Movement Dynamics ◽  
Close Proximity ◽  
Jasus Edwardsii ◽  
Almost All

Previous movement studies on the southern rock lobster (Jasus edwardsii) have all involved releasing tagged animals at the point of capture. In 2007, 5298 lobsters, in total, were tagged and translocated from an offshore site (>100-m depth) to two inshore sites (<20-m depth) in South Australia. After a period of 735 days, 510 (9.6%) had been recaptured. The majority of translocated lobsters were located within close proximity to the release points, with 306 (60%) having moved <5km. Of the remainder, 133 (26%) were recaptured within 5–10km, with a further 71 (14%) individuals having moved >10km. Movement patterns were highly directional in nature, with individuals consistently travelling in a south-west bearing, regardless of distance moved. In almost all cases, movement was from inshore to offshore sites, with female lobsters travelling significantly further (mean 5.66km ±6.41s.d.) than males (mean 5.02km ±9.66s.d.). The results are consistent with previous large-scale tagging studies of J. edwardsii, which indicated high residency levels but with occasional directed movement by some individuals.

The Amethyst Field, Blocks 47/8a, 47/9a, 47/13a, 47/14a, 47,15a, UK North Sea

1991 ◽  
Vol 14 (1) ◽  
pp. 387-393 ◽  
Author(s):  
C. R. Garland
Keyword(s):  
North Sea ◽  
Reservoir Quality ◽  
Geological Model ◽  
Gas Field ◽  
Seismic Parameters ◽  
Recoverable Reserves ◽  
High Production ◽  
Sandstone Formation ◽  
Phased Development ◽  
Overlying Strata

AbstractThe Amethyst gas field was discovered in 1970 by well 47/13-1. Subsequently it was appraised and delineated by 17 wells. It consists of at least five accumulations with modest vertical relief, the reservoir being thin aeolian and fluviatile sandstones of the Lower Leman Sandstone Formation. Reservoir quality varies from poor to good, high production rates being attained from the aeolian sandstones. Seismic interpretation has involved, in addition to conventional methods, the mapping of several seismic parameters, and a geological model for the velocity distribution in overlying strata.Gas in place is currently estimated at 1100 BCF, with recoverable reserves of 844 BCF. The phased development plan envisages 20 development wells drilled from four platforms, and first gas from the 'A' platforms was delivered in October 1990. A unitization agreement is in force between the nine partners, with a technical redetermination of equity scheduled to commence in 1991.

3-D seismic imaging and seismic attribute analysis of genetic sequences deposited in low‐accommodation conditions

Geophysics ◽  
1996 ◽  
Vol 61 (5) ◽  
pp. 1351-1362 ◽  
Author(s):  
B. A. Hardage ◽  
D. L. Carr ◽  
D. E. Lancaster ◽  
J. L. Simmons ◽  
D. S. Hamilton ◽  
...  
Keyword(s):  
Seismic Data ◽  
Sediment Accumulation ◽  
Gas Reservoirs ◽  
Seismic Attribute ◽  
Gas Field ◽  
Reservoir System ◽  
Depositional Processes ◽  
Genetic Sequences ◽  
Data Windows ◽  
Reservoir Facies

A multidisciplinary team, composed of stratigraphers, petrophysicists, reservoir engineers, and geophysicists, studied a portion of Boonsville gas field in the Fort Worth Basin of North‐Central Texas to determine how modern geophysical, geological, and engineering techniques could be combined to understand the mechanisms by which fluvio‐deltaic depositional processes create reservoir compartmentalization in a low‐ to moderate‐accommodation basin. An extensive database involving well logs, cores, production, and pressure data from 200‐plus wells, [Formula: see text] [Formula: see text] of 3-D seismic data, vertical seismic profiles (VSPs), and checkshots was assembled to support this investigation. The reservoir system we studied was the Bend Conglomerate, a productive series of gas reservoirs composed of Middle Pennsylvanian fluvio‐deltaic clastics 900 to 1300 ft (275 to 400 m) thick in our project area. We were particularly interested in this reservoir system because evidence suggested that many of the sequences in this stratigraphic interval were deposited in low‐accommodation conditions (that is, in an environment where there was limited vertical space available for sediment accumulation), and our objective was to investigate how fluvio‐deltaic reservoirs were compartmentalized by low‐accommodation depositional processes. Using an extensive well log database (200 plus wells) and a core‐calibrated calculation of rock facies derived from these logs, we divided the Bend Conglomerate interval into ten genetic sequences, with each sequence being approximately 100 ft (30 m) thick. We then used local VSP and checkshot control to transform log‐measured depths of each sequence boundary to seismic two‐way time coordinates and identified narrow seismic data windows encompassing each sequence across the [Formula: see text] [Formula: see text] 3-D seismic grid. A series of seismic attributes was calculated in these carefully defined data windows to determine which attributes were reliable indicators of the presence of productive reservoir facies and which attributes could, therefore, reveal distinct reservoir compartments and potentially show where infield wells should be drilled to reach previously uncontacted gas reservoirs. Our best success was the seismic attribute correlations we found in the Upper and Lower Caddo sequences, at the top of the Bend Conglomerate. These sequences were deposited in a low‐accommodation setting, relative to other Boonsville sequences, and we found that reflection amplitude and instantaneous frequency, respectively, were reliable indicators of the areal distribution of reservoir facies in these low‐accommodation sequences.

Reserves Determination Using Type Curve Matching and Extended Material Balance Methods in the Medicine Hat Shallow Gas Field

1994 ◽  
Author(s):  
S. L. West ◽  
P. J. R. Cochrane
Keyword(s):  
Material Balance ◽  
Poor Quality ◽  
Curve Matching ◽  
Pressure Data ◽  
Gas Reservoirs ◽  
Shallow Gas ◽  
Gas Field ◽  
Type Curve ◽  
Balance Analysis ◽  
Diffusivity Equation

Tight shallow gas reservoirs in the Western Canada Basin present a number of unique challenges in accurately determining reserves. Traditional methods such as decline analysis and material balance are inaccurate due to the formations' low permeabilities and poor pressure data. The low permeabilities cause long transient periods not easily separable from production decline using conventional decline analysis. The result is lower confidence in selecting the appropriate decline characteristics (exponential or harmonic) which significantly impacts recovery factors and remaining reserves. Limited, poor quality pressure data and commingled production from the three producing zones results in non representative pressure data and hence inaccurate material balance analysis. This paper presents the merit of two new methods of reserve evaluation which address the problems described above for tight shallow gas in the Medicine Hat field. The first method applies type curve matching which combines the analytical pressure solutions of the diffusivity equation (transient) with the empirical decline equation. The second method is an extended material balance which incorporates the gas deliverability theory to allow the selection of appropriate p/z derivatives without relying on pressure data. Excellent results were obtained by applying these two methodologies to ten properties which gather gas from 2300 wells. The two independent techniques resulted in similar production forecasts and reserves, confirming their validity. They proved to be valuable, practical tools in overcoming the various challenges of tight shallow gas and in improving the accuracy in gas reserves determination in the Medicine Hat field.

scholarly journals Evidence for massive emission of methane from a deep‐water gas field during the Pliocene

2020 ◽  
Vol 117 (45) ◽  
pp. 27869-27876
Author(s):  
Martino Foschi ◽  
Joseph A. Cartwright ◽  
Christopher W. MacMinn ◽  
Giuseppe Etiope
Keyword(s):  
Deep Water ◽  
Seismic Data ◽  
Three Dimensional ◽  
Petroleum Industry ◽  
Atmospheric Methane ◽  
Borehole Data ◽  
Gas Field ◽  
Modern Times ◽  
Water Gas ◽  
Fluid Flow Modeling

Geologic hydrocarbon seepage is considered to be the dominant natural source of atmospheric methane in terrestrial and shallow‐water areas; in deep‐water areas, in contrast, hydrocarbon seepage is expected to have no atmospheric impact because the gas is typically consumed throughout the water column. Here, we present evidence for a sudden expulsion of a reservoir‐size quantity of methane from a deep‐water seep during the Pliocene, resulting from natural reservoir overpressure. Combining three-dimensional seismic data, borehole data and fluid‐flow modeling, we estimate that 18–27 of the 23–31 Tg of methane released at the seafloor could have reached the atmosphere over 39–241 days. This emission is ∼10% and ∼28% of present‐day, annual natural and petroleum‐industry methane emissions, respectively. While no such ultraseepage events have been documented in modern times and their frequency is unknown, seismic data suggest they were not rare in the past and may potentially occur at present in critically pressurized reservoirs. This neglected phenomenon can influence decadal changes in atmospheric methane.

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