scholarly journals Optimizing CSG development: Quantitative estimation of lithological and geomechanical reservoir quality parameters from seismic data

2012 ◽  
Vol 2012 (1) ◽  
pp. 1-4 ◽  
Author(s):  
E. Bathellier ◽  
J. Downton ◽  
A. Sena
Keyword(s):  
Seismic Data ◽  
Quantitative Estimation ◽  
Quality Parameters ◽  
Reservoir Quality

Optimising CSG development: quantitative estimation of lithological and geomechanical reservoir quality parameters from seismic data

2012 ◽  
Vol 52 (2) ◽  
pp. 675
Author(s):  
Eric Bathellier ◽  
Jon Downton ◽  
Gabino Castillo
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Quality Parameters ◽  
Azimuthal Anisotropy ◽  
Reservoir Quality ◽  
Valuable Insight ◽  
Fracture Density ◽  
Successful Recovery ◽  
Sweet Spots

Within the past decade, new developments in seismic azimuthal anisotropy have identified a link between fracture density and orientation observed in well logs and the intensity and orientation of the actual anisotropy. Recent studies have shown a correlation between these measurements that provide quantitative estimations of fracture density from 3D wide-azimuth seismic data in tight-gas sand reservoirs. Recent research shows the significance of advanced seismic processing in the successful recovery of reliable fracture estimations, which directly correlates to borehole observations. These quantitative estimations of fracture density provide valuable insight that helps optimise drilling and completion programs, particularly in tight reservoirs. Extending this analysis to CSG reservoirs needs to consider additional reservoir quality parameters while implementing a similar quantitative approach on the interpretation of seismic data and correlation with borehole logging observations. The characterisation of CSG plays involves the understanding of the reservoir matrix properties as well as the in-situ stresses and fracturing that will determine optimal production zones. Pre-stack seismic data can assist with identifying the sweet spots—productive areas—in CSG resource plays by detailed reservoir-oriented gather conditioning followed by pre-stack seismic inversion and multi-attribute analysis. This analysis provides rock property estimations such as Poisson's ratio and Young's modulus, among others, which in turn relate to quantitative reservoir properties such as porosity and brittleness. This study shows an integrated workflow based on pre-stack azimuthal seismic data analysis and well log information to identify sweet spots, estimate geo-mechanical properties, and quantify in-situ principal stresses.

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.

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.

Seismic detection and interpretation of porosity in Carboniferous age rocks of Kansas and Oklahoma

Geophysics ◽  
1989 ◽  
Vol 54 (11) ◽  
pp. 1371-1383 ◽  
Author(s):  
M. R. Thomasson ◽  
R. W. Kettle ◽  
R. M. Lloyd ◽  
R. K. McCormack ◽  
J. P. Lindsey
Keyword(s):  
Seismic Data ◽  
Acoustic Properties ◽  
Reservoir Quality ◽  
Seismic Modeling ◽  
Work Station ◽  
South Central ◽  
Seismic Detection ◽  
Seismic Sections

Many large Mississippian fields (5–130 MMBO) in south‐central Kansas and northern Oklahoma produce from discrete pods of porous chert and dolomite called “chat.” Chat has unusual acoustic properties that allow the porosity pods to be recognized on seismic sections. Integrated geologic and geophysical studies of two analog fields indicate that both reservoir quality and geometry can be interpreted from good quality seismic data. Seismic modeling on an interactive work station plays an important role in developing these interpretations.

Tectonostratigraphic framework of the Lower Keraudren Formation, Bedout Sub-basin: interplay of tectonics and sedimentary systems

2018 ◽  
Vol 58 (2) ◽  
pp. 839 ◽  
Author(s):  
Jon Minken ◽  
Melissa Thompson ◽  
Jack Woodward ◽  
Fred Fernandes ◽  
Rylan Fabrici
Keyword(s):  
Seismic Data ◽  
Middle Triassic ◽  
Sediment Supply ◽  
Sediment Provenance ◽  
Reservoir Quality ◽  
Dispersal Patterns ◽  
North West ◽  
Sediment Dispersal ◽  
Stratigraphic Architecture ◽  
Basin Geometry

Recent drilling activity and new seismic data have contributed to the understanding of the Lower Keraudren Formation in the Bedout Sub-Basin. The Lower Keraudren Formation is a thick (>5 km) succession of strata that was deposited rapidly during the Anisian of the Middle Triassic. Distinctive characteristics related to sediment provenance, sediment supply and accommodation have facilitated subdivision of the Formation into eight informal units: the Milne, Crespin, Baxter, Caley, Hove, Barret, Palma, and Huxley members. Tectonic elements of the East Gondwana Interior Rift and the Bedout High influenced the Sub-basin geometry during deposition of the Lower Keraudren. Extensional tectonics of the East Gondwana Interior Rift generated a series of Palaeozoic tilted fault blocks and grabens, which influenced the stratigraphic architecture, sediment dispersal patterns and distribution of reservoir and source rock facies. The structurally proud Bedout High, a roughly circular (~60 km wide) igneous feature, created a northern boundary to deposition. Seismic stratigraphic interpretation has characterised the interval as a series of north west prograding wedges. Well based data indicates the section is dominated by fluvio-deltaic deposits. Separating the Caley and Hove Members is a significant unconformity that is associated with renewed uplift of the Bedout High and a change in sediment provenance. Chemostratigraphy and petrology indicates the Caley and older strata were derived from a more mature sediment source, whereas the Hove and younger a more immature metamorphic source. Distinct changes in reservoir quality are observed above and below the Caley–Hove unconformity. Below the unconformity, the older, more mature sandstones exhibit superior reservoir quality compared with the younger, more immature sandstones.

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.

THE M. AUSTRALIS SANDSTONES, DAMPIER SUB-BASIN, AUSTRALIA

1999 ◽  
Vol 39 (1) ◽  
pp. 104
Author(s):  
A.J. Crowley
Keyword(s):  
Mass Flow ◽  
Western Australia ◽  
Seismic Data ◽  
Shelf Break ◽  
Oil Fields ◽  
Reservoir Quality ◽  
Flow Deposit ◽  
Basin Floor ◽  
A Minor ◽  
Maximum Flooding Surface

Three independent Barremian sandstone units that are characterised by the M. australis palynozone have been identified in the Lewis Trough and Enderby Terrace of the southeastern Dampier Sub-Basin, offshore Western Australia. They constitute a basin-floor fan unit and shelfal transgressive unit that are characterised by the lower Af. australis sub-zone, a shelfal marine to fluvial unit that is characterised by the middle M. australis sub- zone and a shelfal marine unit that is characterised by the upper M. australis sub-zone. The M. australis sandstones are characterised by their excellent reservoir quality, generally common to abundant glauconite content and common provenance.Core, wireline log and seismic data from wells in the Lewis Trough indicate the sediments characterised by the lower M. australis sub-zone form a mass-flow deposit on the regionally extensive Intra-Muderong Hiatus. Transgressive shelfal greensands, interpreted to lie within the latter part of the lower M. australis sub-zone overlie the In tra-Muderong Hiatus on the Enderby Terrace. The glauconitic sandstones characterised by the middle M. australis sub-zone were deposited during a relative highstand and overlie a maximum flooding surface identified in wells on the southern Enderby Terrace. These deposits form the reservoir section for the Wandoo and Stag oil fields. At Wandoo they form a series of seismically definable progrades, whereas at Stag they are the distal toe-sets that lie sub-parallel to the underlying surface. The subsequent sequence boundary is identified in wells and on seismic data as an erosional surface cutting the underlying sediments. Glauconite-rich, transgressive deposits form a fining-up sequence overlie the sequence boundary. Glauconitic sandstones characterised by the upper M. australis sub-zone were deposited at the palaeo- shelf break during a minor regression.

Prediction of Reservoir Properties from Seismic Data by Multivariate Geostatistics Analysis

2021 ◽  
Author(s):  
Vladimir V. Bezkhodarnov ◽  
Tatiana I. Chichinina ◽  
Mikhail O. Korovin ◽  
Valeriy V. Trushkin
Keyword(s):  
Seismic Data ◽  
Quantitative Estimation ◽  
Clay Content ◽  
Seismic Attributes ◽  
Information Value ◽  
Seismic Exploration ◽  
Rock Properties ◽  
Regression Equations ◽  
Reservoir Properties ◽  
Multivariate Geostatistics

Abstract A new technique has been developed and is being improved, which allows, on the basis of probabilistic and statistical analysis of seismic data, to predict and evaluate the most important parameters of rock properties (including the reservoir properties such as porosity and permeability), that is, oil saturation, effective thicknesses of reservoirs, their sand content, clay content of seals, and others; it is designed to predict the reservoir properties with sufficient accuracy and detail, for subsequent consideration of these estimates when evaluating hydrocarbon reserves and justifying projects for the deposits development. Quantitative reservoir-property prediction is carried out in the following stages: –Optimization of the graph ("scenario") of seismic data processing to solve not only the traditional structural problem of seismic exploration, but also the parametric one that is, the quantitative estimation of rock properties.–Computation of seismic attributes, including exclusive ones, not provided for in existing interpretation software packages.–Estimation of reservoir properties from well logs as the base data.–Multivariate correlation and regression analysis (MCRA) includes the following two stages: Establishing correlations of seismic attributes with estimates of rock properties obtained from well logs.Construction of multidimensional (multiple) regression equations with an assessment of the "information value" of seismic attributes and the reliability of the resulting predictive equations. (By the "informative value" we mean the informativeness quality of the attribute.)–Computation and construction of the forecast map variants, their analysis and producing the resultant map (as the most optimal map version) for each predicted parameter.–Obtaining the resultant forecast maps with their zoning according to the degree of the forecast reliability. The MCRA technique is tested by production and prospecting trusts during exploration and reserves’ estimation of several dozen fields in Western Siberia: Kulginskoye, Shirotnoye, Yuzhno-Tambaevskoye, etc. (Tomsk Geophysical Trust, 1997-2002); Dvurechenskoe, Zapadno-Moiseevskoe, Talovoe, Krapivinskoe, Ontonigayskoe, etc. (TomskNIPIneft, 2002–2013).

Quantitative estimation of compaction and velocity changes using 4D impedance and traveltime changes

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 949-957 ◽  
Author(s):  
Martin Landrø ◽  
Jan Stammeijer
Keyword(s):  
Seismic Data ◽  
Quantitative Estimation ◽  
Time Lapse ◽  
Seismic Velocities ◽  
Compaction Process ◽  
Empirical Relationships ◽  
Velocity Change ◽  
Reservoir Rocks ◽  
Velocity Changes ◽  
4D Seismic

In some hydrocarbon reservoirs, severe compaction of the reservoir rocks is observed. This compaction is caused by production, and it is often associated with changes in the overburden. Time‐lapse (or 4D) seismic data are used to monitor this compaction process. Since the compaction causes changes in both layer thickness and seismic velocities, it is crucial to distinguish between the two effects. Two new seismic methods for monitoring compacting reservoirs are introduced, one based on measured seismic prestack traveltime changes, and the other based on poststack traveltime and amplitude changes. In contrast to earlier methods, these methods do not require additional empirical relationships, such as, for instance, a velocity‐porosity relationship. The uncertainties in estimates for compaction and velocity change are expressed in terms of errors in the traveltime and amplitude measurements. These errors are directly related to the quality and repeatability of time‐lapse seismic data. For a reservoir at 3000‐m depth with 9 m of compaction, and assuming a 4D timeshift error of 0.5 ms at near offset and 2 ms at far offset, we find relative uncertainty in the compaction estimate of approximately 50–60% using traveltime information only.

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