Automated Fault Detection and Extraction under Gas Chimneys Using Hybrid Discontinuity Attributes

3D-seismic data have increasingly shifted seismic interpretation work from a horizons-based to a volume-based focus over the past decade. The size of the identification and mapping work has therefore become difficult and requires faster and better tools. Faults, for instance, are one of the most significant features of subsurface geology interpreted from seismic data. Detailed fault interpretation is very important in reservoir characterization and modeling. The conventional manual fault picking is a time-consuming and inefficient process. It becomes more challenging and error-prone when dealing with poor quality seismic data under gas chimneys. Several seismic attributes are available for faults and discontinuity detection and are applied with varying degrees of success. We present a hybrid workflow that combines a semblance-based fault likelihood attribute with a conventional ant-tracking attribute. This innovative workflow generates optimized discontinuity volumes for fault detection and automatic extraction. The data optimization and conditioning processes are applied to suppress random and coherent noise first, and then a combination of seismic attributes is generated and co-rendered to enhance the discontinuities. The result is the volume with razor sharp discontinuities which are tracked and extracted automatically. Contrary to several available fault tracking techniques that use local seismic continuity like coherency attributes, our hybrid method is based on directed semblance, which incorporates aspects of Dave Hale’s superior fault-oriented semblance algorithm. The methodology is applied on a complex faulted reservoir interval under gas chimneys in a Malaysian basin, yet the results were promising. Despite the poor data quality, the methodology led to detailed discontinuity information with several major and minor faults extracted automatically. This hybrid approach not only improved the fault tracking accuracy but also significantly reduced the fault interpretation time and associated uncertainty. It is equally helpful in detecting any seismic objects like fracture, chimneys, and stratigraphic features.

Download Full-text

Case study: Seismic resolution and reservoir characterization of thin sands using multiattribute analysis and bandwidth extension in the Daqing field, China

Interpretation ◽

10.1190/int-2019-0017.1 ◽

2020 ◽

Vol 8 (1) ◽

pp. T89-T102

Author(s):

David Mora ◽

John Castagna ◽

Ramses Meza ◽

Shumin Chen ◽

Renqi Jiang

Keyword(s):

Seismic Data ◽

Reservoir Characterization ◽

Statistical Significance ◽

Physical Property ◽

Seismic Attributes ◽

Oil Field ◽

Thin Layers ◽

Inversion Method ◽

Bandwidth Extension ◽

Multiattribute Analysis

The Daqing field, located in the Songliao Basin in northeastern China, is the largest oil field in China. Most production in the Daqing field comes from seismically thin sand bodies with thicknesses between 1 and 15 m. Thus, it is not usually possible to resolve Daqing reservoirs using only conventional seismic data. We have evaluated the effectiveness of seismic multiattribute analysis of bandwidth extended data in resolving and making inferences about these thin layers. Multiattribute analysis uses statistical methods or neural networks to find relationships between well data and seismic attributes to predict some physical property of the earth. This multiattribute analysis was applied separately to conventional seismic data and seismic data that were spectrally broadened using sparse-layer inversion because this inversion method usually increases the vertical resolution of the seismic. Porosity volumes were generated using target porosity logs and conventional seismic attributes, and isofrequency volumes were obtained by spectral decomposition. The resulting resolution, statistical significance, and accuracy in the determination of layer properties were higher for the predictions made using the spectrally broadened volume.

Download Full-text

Evaluation of seismic attributes for reservoir characterization over Edi field, Niger delta, Nigeria using 3d seismic data

International Journal of Advanced Geosciences ◽

10.14419/ijag.v8i2.31043 ◽

2020 ◽

Vol 8 (2) ◽

pp. 168

Author(s):

Nyeneime O. Etuk ◽

Mfoniso U. Aka ◽

Okechukwu A. Agbasi ◽

Johnson C. Ibuot

Keyword(s):

Seismic Data ◽

Niger Delta ◽

Reservoir Characterization ◽

Seismic Attributes ◽

3D Seismic ◽

Root Mean Square Amplitude ◽

Phase Gradient ◽

Major Fault ◽

Seismic Sections ◽

3D Seismic Data

Seismic attributes were evaluated over Edi field, offshore Western Niger Delta, Nigeria, via 3D seismic data. Manual mappings of the horizons and faults on the in-lines and cross-lines of the seismic sections were done. Various attributes were calculated and out put on four horizons corresponding to the well markers at different formations within the well were identified. The four horizons identified, which includes: H1, H2, H3 and H4 were mapped and interpreted across the field. The operational agenda was thru picking given faults segments on the in–line of seismic volume. A total of five faults coded as F1, F2, F3, F4 and F5, F1 and F5 were the major fault and were observed as extending through the field. Structural and horizon mappings were used to generate time structure maps. The maps showed the various positions and orientations of the faults. Different attributes which include: root mean square amplitude, instantaneous phase, gradient magnitude and chaos were run on the 3D seismic data. The amplitude and incline magnitude maps indicate direct hydrocarbon on the horizon maps; this is very important in the drilling of wells because it shows areas where hydrocarbons are present in the subsurface. The seismic attributes revealed information, which was not readily apparent in the raw seismic data.

Download Full-text

Geophysical Aspects of Reservoir Characterization of Tight Gas Play in the Dnieper-Donets Basin

10.2118/208501-ms ◽

2021 ◽

Author(s):

Rustem Valiakhmetov ◽

Andrea Murineddu ◽

Murat Zhiyenkulov ◽

Viktor Maliar ◽

Viktor Bugriy ◽

...

Keyword(s):

Data Processing ◽

Seismic Data ◽

Reservoir Characterization ◽

Seismic Attributes ◽

Static Model ◽

Donets Basin ◽

Tight Gas ◽

Seismic Data Processing ◽

Exploration Well

Abstract The objective of this work is to describe a comprehensive approach integrating seismic data processing and sets of wireline logs for reservoir characterization of one of the tight gas plays of the Dnieper-Donets basin. This paper intends to discuss a case study from seismic data processing, integrating seismic attributes with formation properties from logs in a geocellular model for sweet spot selection and risk analysis. The workflow during the project included the following steps.Seismic data 3D processing, including 5D interpolation and PSTM migration.Interpretation of limited log data from 4 exploration and appraisal wells.Seismic interpretation and inversion.Building a static model of the field.Recommendations for drilling locations.Evaluation of the drilled well to verify input parameters of the initial model. The static model integrated all available subsurface data and used inverted seismic attributes calibrated to the available logs to constrain the property modelling. Then various deterministic and stochastic approaches were used for facies modeling and estimation of gas-in-place volume. Integrating all the available data provides insights for better understating the reservoir distribution and provided recommendations for drilling locations. Based on the combination of the geocellular model, seismic attributes and seismic inversion results, the operator drilled an exploration well. The modern set of petrophysical logs acquired in the recently drilled well enforced prior knowledge and delivered a robust picture of the tight gas reservoir. The results from the drilled well matched predicted formation properties very closely, which added confidence in the technical approach applied in this study and similar studies that followed later. It is the fork in the road moment for the Dnieper-Donetsk basin with huge tight gas potential in the region that inspires for exploration of other prospects and plays. A synergy of analytical methods with a combination of seismic processing, geomodeling, and reservoir characterization approaches allowed accurate selection of the drilling targets with minimum risk of "dry hole" that has been vindicated by successful drilling outcome in a new exploration well.

Download Full-text

The application of seismic attributes for reservoir characterization in Pre-Tertiary fractured basement, Vietnam-Malaysia offshore

Interpretation ◽

10.1190/int-2013-0081.1 ◽

2014 ◽

Vol 2 (1) ◽

pp. SA57-SA66 ◽

Cited By ~ 3

Author(s):

Nguyen Huy Ngoc ◽

Sahalan B. Aziz ◽

Nguyen Anh Duc

Keyword(s):

Seismic Data ◽

Reservoir Characterization ◽

Rock Type ◽

Seismic Attributes ◽

Basement Rock ◽

Fracture Density ◽

High Fracture ◽

Offshore Area ◽

Fracture Characteristics ◽

Basement Reservoirs

The Pre-Tertiary fractured basement forms important hydrocarbon-bearing reservoirs in the Vietnam-Malaysia offshore area, and is being produced from such reservoirs in Vietnam where the authors have extensive working experiences for both clastics and fractured basement reservoirs and in both exploration and development phases. Due to their very small matrix porosity, the basement rocks become reservoirs only when they are strongly fractured. The quality of the fractured basement reservoirs depends on basement rock type, fracture density, and fracture characteristics including aperture, azimuth, dip, continuity, and fracture system intersection. Three-dimensional seismic data is applied widely to characterize these basement reservoirs. Based on results from applying many different seismic attributes to 3D seismic data from different Pre-Tertiary fractured basements in Vietnam and Malaysia, we demonstrate the utility of attributes in characterizing fractured basement reservoirs. Seismic attributes help predict the basement rock type and fracture characteristics from near top basement to deep inside basement. In the zone near the top of basement, the characteristics of fracture systems can be predicted by amplitude, coherence, curvature, and secondary derivative attributes. Deep inside the basement, relative acoustic impedance and its attributes have been successfully applied to predict the distribution of high fracture density, while dip and azimuth, ant-tracking, and gradient magnitude attributes have proven to be effective for predicting fracture characteristics. The accuracy of fracture characterization based on seismic attributes has been verified by drilling results.

Download Full-text

Estimation of effective porosity using geostatistics and multiattribute transforms: A case study

Geophysics ◽

10.1190/1.1707054 ◽

2004 ◽

Vol 69 (2) ◽

pp. 352-372 ◽

Cited By ~ 57

Author(s):

A. G. Pramanik ◽

V. Singh ◽

Rajiv Vig ◽

A. K. Srivastava ◽

D. N. Tiwary

Keyword(s):

Seismic Data ◽

Acoustic Impedance ◽

Reservoir Characterization ◽

Middle Eocene ◽

Seismic Attributes ◽

Effective Porosity ◽

3D Seismic ◽

Porosity Distribution ◽

Porosity Logs ◽

3D Seismic Data

The middle Eocene Kalol Formation in the north Cambay Basin of India is producing hydrocarbons in commercial quantity from a series of thin clastic reservoirs. These reservoirs are sandwiched between coal and shale layers, and are discrete in nature. The Kalol Formation has been divided into eleven units (K‐I to K‐XI) from top to bottom. Multipay sands of the K‐IX unit 2–8 m thick are the main hydrocarbon producers in the study area. Apart from their discrete nature, these sands exhibit lithological variation, which affects the porosity distribution. Low‐porosity zones are found devoid of hydrocarbons. In the available 3D seismic data, these sands are not resolved and generate a composite detectable seismic response, making reservoir characterization through seismic attributes impossible. After proper well‐to‐seismic tie, the major stratigraphic markers were tracked in the 3D seismic data volume for structural mapping and carrying out attribute analysis. The 3D seismic volume was inverted to obtain an acoustic impedance volume using a model‐based inversion algorithm, improving the vertical resolution and resolving the K‐IX pay sands. For better reservoir characterization, effective porosity distribution was estimated through different available techniques taking the K‐IX upper sand as an example. Various sample‐based seismic attributes, the impedance volume, and effective porosity logs were used as inputs for this purpose. These techniques are map‐based geostatistical methods using the acoustic impedance volume, stepwise multilinear regression, probabilistic neural networks (PNN) using multiattribute transforms, and a new technique that incorporates both geostatistics and multiattribute transforms (either linear or nonlinear). This paper is an attempt to compare different available techniques for porosity estimation. On comparison, it is found that the PNN‐based approach using ten sample‐based attributes showed highest crosscorrelation (0.9508) between actual and predicted effective porosity logs at eight wells in the study area. After validation, the predicted effective porosity maps for the K‐IX upper sand are generated using different techniques, and a comparison among them is made. The predicted effective porosity map obtained from PNN‐based model provides more meaningful information about the K‐IX upper sand reservoir. In order to give priority to the actual effective porosity values at wells, the predicted effective porosity map obtained from PNN‐based model for the K‐IX upper sand was combined with actual effective porosity values using co‐kriging geostatistical technique. This final map provides geologically more realistic predicted effective porosity distribution and helps in understanding the subsurface image. The implication of this work in exploration and development of hydrocarbons in the study area is discussed.

Download Full-text

Improving seismic fault detection by super-attribute-based classification

Interpretation ◽

10.1190/int-2018-0188.1 ◽

2019 ◽

Vol 7 (3) ◽

pp. SE251-SE267 ◽

Cited By ~ 4

Author(s):

Haibin Di ◽

Mohammod Amir Shafiq ◽

Zhen Wang ◽

Ghassan AlRegib

Keyword(s):

Fault Detection ◽

Seismic Data ◽

Support Vector ◽

3D Seismic ◽

Subsurface Structure ◽

Computationally Efficient ◽

Seismic Fault ◽

Conventional Methods ◽

Fault Interpretation ◽

3D Seismic Data

Fault interpretation is one of the routine processes used for subsurface structure mapping and reservoir characterization from 3D seismic data. Various techniques have been developed for computer-aided fault imaging in the past few decades; for example, the conventional methods of edge detection, curvature analysis, red-green-blue rendering, and the popular machine-learning methods such as the support vector machine (SVM), the multilayer perceptron (MLP), and the convolutional neural network (CNN). However, most of the conventional methods are performed at the sample level with the local reflection pattern ignored and are correspondingly sensitive to the coherent noises/processing artifacts present in seismic signals. The CNN has proven its efficiency in utilizing such local seismic patterns to assist seismic fault interpretation, but it is quite computationally intensive and often demands higher hardware configuration (e.g., graphics processing unit). We have developed an innovative scheme for improving seismic fault detection by integrating the computationally efficient SVM/MLP classification algorithms with local seismic attribute patterns, here denoted as the super-attribute-based classification. Its added values are verified through applications to the 3D seismic data set over the Great South Basin (GSB) in New Zealand, where the subsurface structure is dominated by polygonal faults. A good match is observed between the original seismic images and the detected lineaments, and the generated fault volume is tested usable to the existing advanced fault interpretation tools/modules, such as seeded picking and automatic extraction. It is concluded that the improved performance of our scheme results from its two components. First, the SVM/MLP classifier is computationally efficient in parsing as many seismic attributes as specified by interpreters and maximizing the contributions from each attribute, which helps minimize the negative effects from using a less useful or “wrong” attribute. Second, the use of super attributes incorporates local seismic patterns into training a fault classifier, which helps exclude the random noises and/or artifacts of distinct reflection patterns.

Download Full-text

Accurate reservoir characterization to reduce drilling risk in Khuff-C carbonate, Ghawar field, Saudi Arabia

GeoArabia ◽

10.2113/geoarabia070181 ◽

2002 ◽

Vol 7 (1) ◽

pp. 81-100 ◽

Cited By ~ 1

Author(s):

Shiv N. Dasgupta ◽

Ming-Ren Hong ◽

Ibrahim A. Al-Jallal

Keyword(s):

Inverse Modeling ◽

Seismic Data ◽

Reservoir Characterization ◽

Cyclic Carbonate ◽

Poor Quality ◽

Gas Production ◽

Associated Gas ◽

Reservoir Rocks ◽

Well Data ◽

Ghawar Field

ABSTRACT The Khuff-C reservoir in the Ghawar field is a stratified sequence of cyclic carbonate-evaporite deposits within the Permian Khuff Formation. The reservoir is heterogeneous, complex, and influenced by syndepositional diagenesis. Wells drilled into the Khuff-C in the ‘Uthmaniyah sector of Ghawar are usually prolific producers of non-associated gas but some have intersected poor-quality reservoir intervals with little or no gas production. The Khuff-C reservoir rocks were deposited in a peritidal setting where slight changes in sea level created locally exposed highs. The exposure in an arid climate resulted in outliers of porosity occlusion formed by evaporite cements within the Khuff-C reservoir. The outliers are variably sized and randomly distributed and the challenge is to predict their occurrence in order to avoid them in development drilling. Inverse modeling of the ‘Uthmaniyah 3-D seismic data has identified the tight-porosity outliers as areas of anomalously high acoustic impedance. Integration of 3-D seismic analyses with petrophysical and other well data has improved the reservoir characterization and reduced the drilling risk.

Download Full-text