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.

The role of reservoir modelling in unlocking unconventional (resource) plays

2013 ◽  
Vol 53 (2) ◽  
pp. 440
Author(s):  
Hiwa Sidiq ◽  
Eli Silalahi ◽  
Grant Skinner ◽  
Perdana Noverda Pamurty
Keyword(s):  
Seismic Data ◽  
Fracture Network ◽  
Fracture Density ◽  
Reservoir Modelling ◽  
Technical Developments ◽  
Well Spacing ◽  
Shale Reservoirs ◽  
Unconventional Resource ◽  
Sweet Spots

There has been a recent focus on the insight that reservoir modelling provides into devising the best workflows and its ability to include reservoir attributes that affect recovery factors in shale. This extended abstract examines recent technical developments in reservoir modelling and how such modelling can identify sweet spots in shale reservoirs. An accurate characterisation of a pre-existing fracture network and its structural complexities, however, requires the gathering of a large amount of data. In addition, investigating sweet spots at the presently low gas prices sometimes prevents the acquisition of such data that is essential as an input to drilling strategies, fracture program design, well spacing, and understanding stimulated reservoir volumes (SRV) in shale. The pre-existing fractures may also have a limited impact on recovery rates. The transport along a wellbore is mainly controlled by the drained volume, not only by the fractures around the well. In such cases, the pre-existing fractures and their reactivation during the fracturing stage are not sufficient to determine the amount of gas that can be recovered during production. This volume is effectively not only a function of the fracture density, but also of the propped fractures. The challenge, therefore, becomes the ability to have a good estimation of the size of the SRV and be able to calibrate this volume using relevant data such as micro-seismic data and the recovery from previous fraccing stages. This extended abstract also discusses how reservoir modelling can play a key role in this area.

A Novel Workflow to Invert Broadband Seismic Data: A Case Study from Onshore Fields in Abu Dhabi

2021 ◽  
Author(s):  
M. Afia ◽  
A. Mukherjee ◽  
A. Glushchenko ◽  
R. Elsayed ◽  
M. Paydayesh ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Seismic Data ◽  
Full Range ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Well Test ◽  
Low Frequencies ◽  
High Frequencies ◽  
Successful Recovery

Abstract Broadband seismic data has several benefits for quantitative seismic reservoir characterization. It is characterized by a significant increase of seismic frequency bandwidth on both the low and high sides of the frequency spectra. This work presents a novel seismic inversion approach to exploit the full value out of broadband seismic data. The average wavelet from broadband seismic data is limited in high and low frequencies due to the short duration of the well log and the misalignment of the seismic data with the well-log synthetic at high frequencies. Limitation of the extracted wavelet and optimization can generate band-limited inversion results that do not capture the full range of frequencies. An alternate approach of dividing the data into three frequency bands resulted in extracted wavelets that capture the spectrum of each band, and in turn produced a reliable broadband inversion result honoring the full range of frequencies present in the data. Inversion results gave a superior match of the estimated synthetic with the data spectra (Figure 1), and the reservoir was better calibrated at all the well locations. Successful recovery of the ultra-low frequencies enabled us to maximize the value of the broadband data. The workflow also pushed the frequency of the inverted properties to 80 Hz which helped in turn to characterize some of the relatively thinner layers, which were otherwise getting averaged out. Building a low frequency model for AVO seismic inversion using ultra-low frequency information leads to a significant improvement of predictability away from wells. As a prior model, a geologically constrained (4 Hz) low frequency filter was applied. Review of the broadband AVO seismic inversion results clearly indicate a better match between the inverted traces and well log properties at the studied wells. Also, the blind well test results at four wells indicate an excellent match to the blind well logs, which adds a high degree of confidence on the inverted elastic properties. Also, the synthetic spectra of the ultra-low and ultra-high frequencies is captured and maintained in the inverted broadband seismic data. The novelty of the new workflow is in the ability to effectively invert the broad frequency band of seismic data. Successful recovery of the ultra-low and ultra-high frequencies enabled us to maximize the value of the broadband data. Subsequently, the high frequency elastic properties helped in successful characterization of thinner reservoirs and will help in better optimization of the future field development initiatives.

Spectro-mechanical Characterization of Aromaticity and Maturity of Kerogens in Oil Shale at 6 nm Spatial Resolution

2018 ◽  
Author(s):  
Devon Jakob ◽  
Le Wang ◽  
Haomin Wang ◽  
Xiaoji Xu
Keyword(s):  
Spatial Resolution ◽  
Oil Shale ◽  
Infrared Imaging ◽  
Mechanical Characterization ◽  
Optical Diffraction ◽  
Chemical Compositions ◽  
Valuable Insight ◽  
Eagle Ford Shale

<p>In situ measurements of the chemical compositions and mechanical properties of kerogen help understand the formation, transformation, and utilization of organic matter in the oil shale at the nanoscale. However, the optical diffraction limit prevents attainment of nanoscale resolution using conventional spectroscopy and microscopy. Here, we utilize peak force infrared (PFIR) microscopy for multimodal characterization of kerogen in oil shale. The PFIR provides correlative infrared imaging, mechanical mapping, and broadband infrared spectroscopy capability with 6 nm spatial resolution. We observed nanoscale heterogeneity in the chemical composition, aromaticity, and maturity of the kerogens from oil shales from Eagle Ford shale play in Texas. The kerogen aromaticity positively correlates with the local mechanical moduli of the surrounding inorganic matrix, manifesting the Le Chatelier’s principle. In situ spectro-mechanical characterization of oil shale will yield valuable insight for geochemical and geomechanical modeling on the origin and transformation of kerogen in the oil shale.</p>

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

scholarly journals Batch seismic inversion using the iterative ensemble Kalman smoother

2021 ◽  
Author(s):  
Michael Gineste ◽  
Jo Eidsvik
Keyword(s):  
Seismic Data ◽  
Time Windows ◽  
Seismic Inversion ◽  
Efficient Estimation ◽  
Solution Strategy ◽  
Adaptive Procedure ◽  
Waveform Data ◽  
Kalman Smoother ◽  
Seismic Waveform ◽  
Single Batch

AbstractAn ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and efficient estimation. The adaptivity relies on an analysis of the update direction used in the iterative procedure, and an interpretation of contributions from prior and likelihood to this update. The idea is that these must balance; if the prior dominates, the estimation process is inefficient while the estimation is likely to overfit and diverge if data dominates. Two approaches to meet this balance are formulated and evaluated. One is based on an interpretation of eigenvalue distributions and how this enters and affects weighting of prior and likelihood contributions. The other is based on balancing the norm magnitude of prior and likelihood vector components in the update. Only the latter is found to sufficiently regularize the data window. Although no guarantees for avoiding ensemble divergence are provided in the paper, the results of the adaptive procedure indicate that robust estimation performance can be achieved for ensemble-based inversion of seismic waveform data.

Acquire Ocean Bottom Seismic Data and Time-Lapse Geochemistry Data Simultaneously to Identify Compartmentalization and Map Hydrocarbon Movement

2021 ◽  
Author(s):  
Rick Schrynemeeckers
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Time Lapse ◽  
Field Trials ◽  
Detection Methods ◽  
Ocean Bottom ◽  
Field Development ◽  
Dense Grid ◽  
Hydrocarbon Charge ◽  
Sweet Spots

Abstract Current offshore hydrocarbon detection methods employ vessels to collect cores along transects over structures defined by seismic imaging which are then analyzed by standard geochemical methods. Due to the cost of core collection, the sample density over these structures is often insufficient to map hydrocarbon accumulation boundaries. Traditional offshore geochemical methods cannot define reservoir sweet spots (i.e. areas of enhanced porosity, pressure, or net pay thickness) or measure light oil or gas condensate in the C7 – C15 carbon range. Thus, conventional geochemical methods are limited in their ability to help optimize offshore field development production. The capability to attach ultrasensitive geochemical modules to Ocean Bottom Seismic (OBS) nodes provides a new capability to the industry which allows these modules to be deployed in very dense grid patterns that provide extensive coverage both on structure and off structure. Thus, both high resolution seismic data and high-resolution hydrocarbon data can be captured simultaneously. Field trials were performed in offshore Ghana. The trial was not intended to duplicate normal field operations, but rather provide a pilot study to assess the viability of passive hydrocarbon modules to function properly in real world conditions in deep waters at elevated pressures. Water depth for the pilot survey ranged from 1500 – 1700 meters. Positive thermogenic signatures were detected in the Gabon samples. A baseline (i.e. non-thermogenic) signature was also detected. The results indicated the positive signatures were thermogenic and could easily be differentiated from baseline or non-thermogenic signatures. The ability to deploy geochemical modules with OBS nodes for reoccurring surveys in repetitive locations provides the ability to map the movement of hydrocarbons over time as well as discern depletion affects (i.e. time lapse geochemistry). The combined technologies will also be able to: Identify compartmentalization, maximize production and profitability by mapping reservoir sweet spots (i.e. areas of higher porosity, pressure, & hydrocarbon richness), rank prospects, reduce risk by identifying poor prospectivity areas, accurately map hydrocarbon charge in pre-salt sequences, augment seismic data in highly thrusted and faulted areas.

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.

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