砂泥岩薄互层的高分辨率地震储层反演预测
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摘要
论文以作完成的三维高分辨地震资料的研究实践为基础,研究了高分辨率地震储层反演和储层预测的原理、方法及应用效果,重点研究高分辨率地震的反演处理方法理论及预测技术。其主要目的是针对陆相盆地的特殊性以及所限的资料条件,综合应用高分辨率地震、地质、测井、钻井、试油试采等多种信息开展高分辨率地震反演、储层预洲和油气检测等方面研究,探索出一条适合陆相地质条件的砂泥岩薄互层预测的切实可行的方法技术,从而达到提高储层和油气预测精度的目的。
研究工作中原油田文一濮地区三维高分辨率地震资料为基础,进行高分辨率三维地震的储层反演预测及相关内容的研究。主要研究内容为:
通过砂泥岩互层模型试验,分析了不同的砂泥岩组合的地震反射特征以及其分辨储层的能力,分析认为,薄互层反射波的振幅强弱主要由物性差异决定,频率对薄层厚度的敏感度与地层厚度成正比,反射波具有振幅调谐和频率调谐作用。
采用先进的技术进行三维高分辨率采集、处理和解释,提高了地震资料的分辨率和解释精度。高分辨率采集主要采用“三高”、“二低”、“二小”、“二新”技术;高分辨处理,采用了一系列提高信噪比的方法,应用了有效的展信号频带的方法等。使地震剖面的分辨率有了大幅度的提高,原有资料的频率平均提高10-15Hz,主频在2500米处达到80Hz,频宽达到150Hz,为薄层的识别和岩性研究以及小断层和小断块的解释提供了可靠的资料。高分辨率解释方面,分别采用分步解释方法和高分辨率全三位儿解释方法、以及小幅度构造精细成图技术,分别从宏观地质体解释与小地质体解释相结合,从点面结合到三维立体空间相结合的方法。充分利用高分辨率地震资料同相轴多,分辨能力高的特点,进行构造精细解释,发挥了高分辨率地震资料的优势。
在高分辨率地震资料的储层反演方面,进行高分辨率反演方法研究,对影响反演结果的各项参数,相关资料的预处理、初始模型的建立、子波分析等进行了系统地分析研究,在此基础上提出了提高地震反演精度和分辨率的多分辨率、分步反演的方法和技术思路。
最后对储层的综合解释方法进行了系统研究,提出了包括数据驱动、门槛值法等几种更加有效的储层参数解释方法,有效地提高了储层参数的解释精度。应用砂泥岩互层的高分辨率储层反演和预测技术,预测储层的能力大大增强,反演资料基本上能够解释10米左右的薄层。
Thesis takes the research completed by author using three-dimensional high resolution seismic data as foundation, studying the technology, theory and effect of high resolution seismic reservoir inversion and prospect. As the important aspect, the high resolution inversion technique and prospect technology are studying. The main objective is studying high resolution inversion technique and prospect technology integrating high resolution seismic data, geologic data logging, drilling, testing etc, aim at the special of the basin and the data condition limit. And that, finding an effective prospect method and technology that adapt to the thin interbcds of sandstone and mudstone, so that the precision of oil and gas prospect can be improved .
The study work is based on the 3D high resolution seismic data of wen pu region, carrying on 3D high resolution seismic study of inversion and prospect and related contents: Analyzing the §eismic reflect characteristic of the different combination of sandstone and mudstone and it distinguishes ability to the reservoir, through the model experiment of the thin interbeds of sandstone and mudstone. Analyzing to think, the amplitude of the seismic mainly is decided by the thing difference, the frequency becomes the direct proportion to the sensitive degree and the geologic layer thicknesses of the thin layer thickness, reflecting wave has the tune domino effect of the amplitude and frequency.
The forerunner's technique are adopted to carry on the three high resolution seismic acquisition processing and interpretation so that the seismic resolution and accuracy were improved, the "three high" "two low" "two small" "two new" technique are adopted in high resolution seismic acquisition and some methods are adopted to improve the sign-to-noise such as expending the width of sign etc .Make the resolution of seismic section had the significant exaltation, the frequency of the original data raise the 10-15 Hz equally, the main frequency attain the 80 Hz in 2500 meters, the bandwidth attain the 150 Hz, identifying for the thin interbcds of sandstone and mudstone and small faulted blocks hermeneutic to provide the dependable data. In the aspect of the high resolution interpretation, adopting to tread to interpretation method and high resolutions separately respectively real three interpretation method, and small range structure is fine the diagram technique, interpretation to interpretation with small geology body to combine together from the macroscopic geology body respectively, from order to face to combine three method that stereoscopic space of 3D combine together.
In the high resolution seismic inversion aspect, carrying on the high resolution inversion method research, such as the various parameters of result towards affect inversion, the primal model establishment, the wavelet analysis, etc. putting forward the method and the technique way of multi-resolutions inversion to exaltatcd seismic inversion accuracy and resolution
Integrating interpretation method is carryed on finally, put forward to include the dala to drive, the threshold is worth the method etc. several kinds keep the reservoir parameter to interpretation the method more and effectively, raising to keep hermeneutic accuracy of the reservoir parameter availably.
'The applied of the high resolution seismic inversion and prospect technique, the ability that predicts reservoir strengthens consumedly, the inversion data can interpretate the or so and thin bed of 10 meters basically.
研究工作中原油田文一濮地区三维高分辨率地震资料为基础,进行高分辨率三维地震的储层反演预测及相关内容的研究。主要研究内容为:
通过砂泥岩互层模型试验,分析了不同的砂泥岩组合的地震反射特征以及其分辨储层的能力,分析认为,薄互层反射波的振幅强弱主要由物性差异决定,频率对薄层厚度的敏感度与地层厚度成正比,反射波具有振幅调谐和频率调谐作用。
采用先进的技术进行三维高分辨率采集、处理和解释,提高了地震资料的分辨率和解释精度。高分辨率采集主要采用“三高”、“二低”、“二小”、“二新”技术;高分辨处理,采用了一系列提高信噪比的方法,应用了有效的展信号频带的方法等。使地震剖面的分辨率有了大幅度的提高,原有资料的频率平均提高10-15Hz,主频在2500米处达到80Hz,频宽达到150Hz,为薄层的识别和岩性研究以及小断层和小断块的解释提供了可靠的资料。高分辨率解释方面,分别采用分步解释方法和高分辨率全三位儿解释方法、以及小幅度构造精细成图技术,分别从宏观地质体解释与小地质体解释相结合,从点面结合到三维立体空间相结合的方法。充分利用高分辨率地震资料同相轴多,分辨能力高的特点,进行构造精细解释,发挥了高分辨率地震资料的优势。
在高分辨率地震资料的储层反演方面,进行高分辨率反演方法研究,对影响反演结果的各项参数,相关资料的预处理、初始模型的建立、子波分析等进行了系统地分析研究,在此基础上提出了提高地震反演精度和分辨率的多分辨率、分步反演的方法和技术思路。
最后对储层的综合解释方法进行了系统研究,提出了包括数据驱动、门槛值法等几种更加有效的储层参数解释方法,有效地提高了储层参数的解释精度。应用砂泥岩互层的高分辨率储层反演和预测技术,预测储层的能力大大增强,反演资料基本上能够解释10米左右的薄层。
Thesis takes the research completed by author using three-dimensional high resolution seismic data as foundation, studying the technology, theory and effect of high resolution seismic reservoir inversion and prospect. As the important aspect, the high resolution inversion technique and prospect technology are studying. The main objective is studying high resolution inversion technique and prospect technology integrating high resolution seismic data, geologic data logging, drilling, testing etc, aim at the special of the basin and the data condition limit. And that, finding an effective prospect method and technology that adapt to the thin interbcds of sandstone and mudstone, so that the precision of oil and gas prospect can be improved .
The study work is based on the 3D high resolution seismic data of wen pu region, carrying on 3D high resolution seismic study of inversion and prospect and related contents: Analyzing the §eismic reflect characteristic of the different combination of sandstone and mudstone and it distinguishes ability to the reservoir, through the model experiment of the thin interbeds of sandstone and mudstone. Analyzing to think, the amplitude of the seismic mainly is decided by the thing difference, the frequency becomes the direct proportion to the sensitive degree and the geologic layer thicknesses of the thin layer thickness, reflecting wave has the tune domino effect of the amplitude and frequency.
The forerunner's technique are adopted to carry on the three high resolution seismic acquisition processing and interpretation so that the seismic resolution and accuracy were improved, the "three high" "two low" "two small" "two new" technique are adopted in high resolution seismic acquisition and some methods are adopted to improve the sign-to-noise such as expending the width of sign etc .Make the resolution of seismic section had the significant exaltation, the frequency of the original data raise the 10-15 Hz equally, the main frequency attain the 80 Hz in 2500 meters, the bandwidth attain the 150 Hz, identifying for the thin interbcds of sandstone and mudstone and small faulted blocks hermeneutic to provide the dependable data. In the aspect of the high resolution interpretation, adopting to tread to interpretation method and high resolutions separately respectively real three interpretation method, and small range structure is fine the diagram technique, interpretation to interpretation with small geology body to combine together from the macroscopic geology body respectively, from order to face to combine three method that stereoscopic space of 3D combine together.
In the high resolution seismic inversion aspect, carrying on the high resolution inversion method research, such as the various parameters of result towards affect inversion, the primal model establishment, the wavelet analysis, etc. putting forward the method and the technique way of multi-resolutions inversion to exaltatcd seismic inversion accuracy and resolution
Integrating interpretation method is carryed on finally, put forward to include the dala to drive, the threshold is worth the method etc. several kinds keep the reservoir parameter to interpretation the method more and effectively, raising to keep hermeneutic accuracy of the reservoir parameter availably.
'The applied of the high resolution seismic inversion and prospect technique, the ability that predicts reservoir strengthens consumedly, the inversion data can interpretate the or so and thin bed of 10 meters basically.
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[1] Examples of multi-attribute, neural network-based seismic object detection de Groot, P., Ligtenberg, H., Oldenziel, T., Connolly, D. and Meldahl, P. (Statoil), 2004.In GS Memoir No. 29 "3D Seismic Technology; Application to the Exploration of Sedimentary Basins." Editors Davies, R.J., Cartwright, J.A., Stewart, S.A, Lappin, M. and Underhill, J.R.
[2] Identifying seismic objects by their texture, orientation and size: A new interpretation tool Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, A., de Groot, P. and Aminzadeh, F., 2003. AAPG Annual Meeting, Extended abstract, Houston
[3]An iterative method for identifying seismic objects by their texture, orientation and size Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, A., de Groot, P., 2001. 71st SEG conference, Extended abstract, San Antonio
[4]Semi-automated object detection in 3-D seismic data Tingdahl, K., de Groot, P. and Heggland, R. (Statoil), 2001. Offshore, August 2001
[5] Selecting and combining attributes to enhance detection of seismic objects de Groot, P., Ligtenberg, H., Meldahl, P. (Statoil) and Heggland, R. (Statoil), 2001. 63rd EAGE conference, Extended abstract, Amsterdam
[6] Detection of seismic objects, the fastest way to do prospect and geohazard evaluation Heggland, R. (Statoil), Meldahl, P. (Statoil), de Groot, P. and Bril, A., 2001. 63rd EAGE conference, Extended abstract, Amsterdam
[7] Semi-automated detection of seismic objects by directive attributes and neural networks, method and applications Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, B. and de Groot, P., 2000. 62nd EAGE conference, Extended abstract, Glasgow
[8]Definition of geohazards in exploration 3-D seismic data using attributes and neural-network analysis Heggland, R., 2004.AAPG Bulletin, Special Theme Issue: High-resolution studies of continental margin geology and geohazards Volume 88, No. 6, June 2004.
[9]Unravelling the petroleum system by enhancing fluid migration paths in seismic data using a neural network based pattern recognition technique Ligtenberg, H., 2003.Geofluids magazine, 3, November 2003, pp 255-261.
[10] Application of gas chimney technology in the Lamprea area, offshore GOM Alvarado, J., Aminzadeh, F. and Connolly, D., 2003.SEG annual meeting, Dallas. Extended abstract.
[11] Sealing quality analysis of faults and formations by means of seismic attributes and neural networks Ligtenberg, H., 2003.EAGE Fault and Top Seal conference, Montpellier, France
[12] Fluid migration path detection and its application to basin modelling Ligtenberg, H. and Thomsen, R. (Maersk Oil), 2003.65th EAGE conference, Stavanger, Norway
[13] Fluid migration path detection in seismic data, a valuable tool in oil and gas exploration Ligtenberg, H., 2003.European Geologist Magazine, volume 15, June 2003
[14]Chimney detection and interpretation - revealing sealing quality of faults, geohazards, charge of and leakage from reservoirs Ligtenberg, H. and Connolly, D., 2003.Journal of Geochemical Exploration, May 2003. Contribution to Geofluids IV, Fourth international conference on fluid evolution, migration and interaction in sedimentary basins and orogenic belts, Utrecht,the Netherlands.
[15]Vertical Hydrocarbon Migration at the Nigerian Continental Slope: Applications of Seismic Mapping Techniques Heggland, R. (Statoil), 2003. AAPG conference, Salt Lake City
[16] Application of Chimney Cubes in the Design of Geochemical Surveys Heggland, R. (Statoil), 2003.AAPG conference, Salt Lake City
[17]Looking for gas chimneys and faults Aminzadeh, F. and Connolly, D., 2002.AAPG Explorer, 23, nr 12, December 2002, pp 20-21
[18] Geohazard detection and other applications of chimney cubes Aminzadeh, F., Connolly, D., Heggland, R. (Statoil), Meldahl, P. (Statoil) and de Groot, P., 2002.The leading Edge, July 2002, pp 681-685
[19]Seismic evidence of vertical fluid migration through faults, Applications of Chimney and Fault detection Heggland, R. (Statoil), 2002.AAPG Hedberg Conference, Vancouver, BC, Canada
[20]Determining migration path from seismically derived gas chimneys Aminzadeh, F., de Groot, P., Berge, T. (Forest Oil), Oldenziel, T. and Ligtenberg, H., 2002.AAPG Hedberg Conference, Vancouver, BC, Canada
[21] Examples of seismic chimney detection and interpretation Ligtenberg, H., 2001. Subsurface sediment mobilisation conference, Ghent, Belgium
[22] Seismic expression of hydrocarbon accumulations and seeps de Groot, P., 2001.ESF workshop Delft, August 2001
[23]Mapping of fluid flow by the use of 3D seismic data and special processing Heggland, R. (Statoil), 2001.ESF workshop Delft, August 2001
[24]Using gas chimneys as an exploration tool, Part 1 and Part 2 Aminzadeh, F., de Groot, P., Berge, T. (Forest Oil) and Valenti, G. (AGIP), 2001. World Oil, May 2001
[25] Identifying faults and gas chimney using multiattributes and neural networks Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, A. and de Groot, P., 2001.The Leading Edge, May 2001
[26]Detection of Seismic Chimneys by neural networks, a New Prospect Evaluation Tool Heggland, R. (Statoil), Meldahl, P. (Statoil), Bril, A. and de Groot, P. 2000. 62nd EAGE conference, Glasgow
[27]Seismic chimney interpretation examples from the North Sea and the Gulf of Mexico Heggland, R. (Statoil), Meldahl, P. (Statoil), de Groot, P. and Aminzadeh, F., 2000. American Oil & Gas Reporter, February 2000
[28]The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 1; Methodology Meldahl, P. (Statoil), Heggland, R. (Statoil), de Groot, P. and Bril, A., 1999.69th SEG conference in Houston
[29]The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 2; Interpretation Meldahl, P. (Statoil), Heggland, R. (Statoil), de Groot, P. and Bril, A., 1999.69th SEG conference in Houston
[30]Meta-attributes - the key to multivolume, multiattribute interpretation de Rooij, M. and Tingdahl, K., 2002. The Leading Edge, October 2002, pp 1050-1053
[31]Fracture detection from pre-stack p-wave data de Rooij, M., Hemstra, N. and van Boom, R. (Wintershall), 2002.64th EAGE annual conference, Florence, Italy
[32]Semi-automatic detection of faults in 3-D seismic signals Tingdahl, K., Steen, O. (Statoil), Meldahl, P. (Statoil) and Ligtenberg, H., 2001.71st SEG conference, Houston
[33]Time-lapse seismic within reservoir engineering Oldenziel, T., 2003. PhD thesis, Delft University of Technology, pp. 204.
[34]Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia Ligtenberg, H. and Wansink, G. (formerly dGB), 2002.In: Nikravesh, M., Aminzadeh, F., and Zadeh, L.A. (Eds.), 2002. Soft computing and intelligent data analysis in oil exploration. Developments in Petroleum Science, Volume 51. Chapter 19, pp 397
[35] Seismic reservoir characterization with limited well control Oldenziel, T., Aminzadeh, F., de Groot, P. and Nielsen, S. (GeoInfo; dGB agent), 2002. EXPOGEF, 7th International SBGf in Bahia, Brasil
[36]Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia Ligtenberg, H. and Wansink, G. (formerly dGB), 2001.Journal of Petroleum Geology JPG, vol.24(4), October 2001, pp 389
[37]Prediction of static and dynamic parameters from time-lapse 3-D seismic Oldenziel, T., de Groot, P. and Kvamme, L. (formerly Statoil), 2000.62st EAGE Conference, Glasgow
[38]Estimation of Reservoir Properties by Monte Carlo Simulation Nakayama, K. (JGI; dGB agent), 2000. SPE Asia Pacific Conference on Integrated Modelling for Asset Management, Yokohama
[39]Neural network-based prediction of porosity and water saturation from time-lapse seismic; a case study Oldenziel, T., de Groot, P. and Kvamme, L. (formerly Statoil), 2000.First Break, February 2000.
[40]Volume Transformation by way of Neural Network Mapping de Groot, P., 1999. 61st EAGE Conference, Helsinki
[41]Seismic Reservoir Characterisation Using Artificial Neural Networks de Groot, P., 1999. 19th Mintrop seminar, Muenster, Germany
[42]Unsupervised segmentation in seismic data analysis Grennberg Fismen, B., Clausen, S., Yang, L., Carlin, M., Kavli, T. (all Sintef) and Wansink, G. (formerly dGB), 2001. ICIP conference in Thessaloniki, Greece
[43]A new confidence bound estimation method for neural networks, an application example Wansink, G. (formerly dGB), Yang, L. (Sintef), et al., 2001. 63rd EAGE conference, Amsterdam
[44]An evaluation of confidence bound estimation methods for neural networks Yang, L. (Sintef), et al., 2000. presented at ESIT
[45]Reservoir parameter estimation using a hybrid neural network Aminzadeh, F., et al., 2000. Computer and Geoscience
[46]Neural networks introduction A document from 1998 describing Multi-Layer-Perceptrons, Radial-Basis-Functions and Unsupervised-Vector-Quantiser.
[47]Stochastic inversion of seismic data by evaluation of pseudo-wells Bril, A. and de Groot, P., 1998. 60th EAGE conference, Leipzig
[48]Analysing and simulating stratigraphic patterns using Markov Chain analysis Bekkevold, J. and Bril, A., 1998 60th EAGE conference, Leipzig
[49]dGB-GDI Introduction A general introduction to dGB-GDI including the integration framework concept, the well simulator and neural networks.
[50]Monte Carlo statistics A document from 1997 on simulating correlated multi-variate stochastic variables. Be prepared for some heavy mathematics.
[51]Rock-physics relationships A document from 1997 explaining Gassmann's equation and some other basic equations used
in rock.
[52]The use of pseudo-wells in seismic interpretation studies de Groot, P. and Bril, A., 1996. 58th EAGE conference in Amsterdam
[53]Dealing with the geoscientific scaling problem - an Object-Oriented approach de Groot, P. and Bril, A., 1996. 1996 EAGE Winter Symposium
[54] Soft computing and intelligent data analysis in oil exploration Nikravesh, M., Aminzadeh, F. and Zadeh, L.A., ed., 2003. With contributions from Ligtenberg, H. and Wansink, A.G. and Tingdahl, K. Developments in pertoleum science series, vol. 51. Elsevier.
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[56]Challenges direct future of geophysics Aminzadeh, F., 2000. Special millenium edition of the American Oil and Gas Journal
[57] Quantitative interpretation generally needs no seismic attributes de Groot, P. and Bril, A., 1997. 59th EAGE conference, Geneva
[58] Should we store all data in standardized data stores? Bril, A. and de Groot, P., 1996. 58th EAGE conference, Amsterdam
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[6] Detection of seismic objects, the fastest way to do prospect and geohazard evaluation Heggland, R. (Statoil), Meldahl, P. (Statoil), de Groot, P. and Bril, A., 2001. 63rd EAGE conference, Extended abstract, Amsterdam
[7] Semi-automated detection of seismic objects by directive attributes and neural networks, method and applications Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, B. and de Groot, P., 2000. 62nd EAGE conference, Extended abstract, Glasgow
[8]Definition of geohazards in exploration 3-D seismic data using attributes and neural-network analysis Heggland, R., 2004.AAPG Bulletin, Special Theme Issue: High-resolution studies of continental margin geology and geohazards Volume 88, No. 6, June 2004.
[9]Unravelling the petroleum system by enhancing fluid migration paths in seismic data using a neural network based pattern recognition technique Ligtenberg, H., 2003.Geofluids magazine, 3, November 2003, pp 255-261.
[10] Application of gas chimney technology in the Lamprea area, offshore GOM Alvarado, J., Aminzadeh, F. and Connolly, D., 2003.SEG annual meeting, Dallas. Extended abstract.
[11] Sealing quality analysis of faults and formations by means of seismic attributes and neural networks Ligtenberg, H., 2003.EAGE Fault and Top Seal conference, Montpellier, France
[12] Fluid migration path detection and its application to basin modelling Ligtenberg, H. and Thomsen, R. (Maersk Oil), 2003.65th EAGE conference, Stavanger, Norway
[13] Fluid migration path detection in seismic data, a valuable tool in oil and gas exploration Ligtenberg, H., 2003.European Geologist Magazine, volume 15, June 2003
[14]Chimney detection and interpretation - revealing sealing quality of faults, geohazards, charge of and leakage from reservoirs Ligtenberg, H. and Connolly, D., 2003.Journal of Geochemical Exploration, May 2003. Contribution to Geofluids IV, Fourth international conference on fluid evolution, migration and interaction in sedimentary basins and orogenic belts, Utrecht,the Netherlands.
[15]Vertical Hydrocarbon Migration at the Nigerian Continental Slope: Applications of Seismic Mapping Techniques Heggland, R. (Statoil), 2003. AAPG conference, Salt Lake City
[16] Application of Chimney Cubes in the Design of Geochemical Surveys Heggland, R. (Statoil), 2003.AAPG conference, Salt Lake City
[17]Looking for gas chimneys and faults Aminzadeh, F. and Connolly, D., 2002.AAPG Explorer, 23, nr 12, December 2002, pp 20-21
[18] Geohazard detection and other applications of chimney cubes Aminzadeh, F., Connolly, D., Heggland, R. (Statoil), Meldahl, P. (Statoil) and de Groot, P., 2002.The leading Edge, July 2002, pp 681-685
[19]Seismic evidence of vertical fluid migration through faults, Applications of Chimney and Fault detection Heggland, R. (Statoil), 2002.AAPG Hedberg Conference, Vancouver, BC, Canada
[20]Determining migration path from seismically derived gas chimneys Aminzadeh, F., de Groot, P., Berge, T. (Forest Oil), Oldenziel, T. and Ligtenberg, H., 2002.AAPG Hedberg Conference, Vancouver, BC, Canada
[21] Examples of seismic chimney detection and interpretation Ligtenberg, H., 2001. Subsurface sediment mobilisation conference, Ghent, Belgium
[22] Seismic expression of hydrocarbon accumulations and seeps de Groot, P., 2001.ESF workshop Delft, August 2001
[23]Mapping of fluid flow by the use of 3D seismic data and special processing Heggland, R. (Statoil), 2001.ESF workshop Delft, August 2001
[24]Using gas chimneys as an exploration tool, Part 1 and Part 2 Aminzadeh, F., de Groot, P., Berge, T. (Forest Oil) and Valenti, G. (AGIP), 2001. World Oil, May 2001
[25] Identifying faults and gas chimney using multiattributes and neural networks Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, A. and de Groot, P., 2001.The Leading Edge, May 2001
[26]Detection of Seismic Chimneys by neural networks, a New Prospect Evaluation Tool Heggland, R. (Statoil), Meldahl, P. (Statoil), Bril, A. and de Groot, P. 2000. 62nd EAGE conference, Glasgow
[27]Seismic chimney interpretation examples from the North Sea and the Gulf of Mexico Heggland, R. (Statoil), Meldahl, P. (Statoil), de Groot, P. and Aminzadeh, F., 2000. American Oil & Gas Reporter, February 2000
[28]The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 1; Methodology Meldahl, P. (Statoil), Heggland, R. (Statoil), de Groot, P. and Bril, A., 1999.69th SEG conference in Houston
[29]The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 2; Interpretation Meldahl, P. (Statoil), Heggland, R. (Statoil), de Groot, P. and Bril, A., 1999.69th SEG conference in Houston
[30]Meta-attributes - the key to multivolume, multiattribute interpretation de Rooij, M. and Tingdahl, K., 2002. The Leading Edge, October 2002, pp 1050-1053
[31]Fracture detection from pre-stack p-wave data de Rooij, M., Hemstra, N. and van Boom, R. (Wintershall), 2002.64th EAGE annual conference, Florence, Italy
[32]Semi-automatic detection of faults in 3-D seismic signals Tingdahl, K., Steen, O. (Statoil), Meldahl, P. (Statoil) and Ligtenberg, H., 2001.71st SEG conference, Houston
[33]Time-lapse seismic within reservoir engineering Oldenziel, T., 2003. PhD thesis, Delft University of Technology, pp. 204.
[34]Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia Ligtenberg, H. and Wansink, G. (formerly dGB), 2002.In: Nikravesh, M., Aminzadeh, F., and Zadeh, L.A. (Eds.), 2002. Soft computing and intelligent data analysis in oil exploration. Developments in Petroleum Science, Volume 51. Chapter 19, pp 397
[35] Seismic reservoir characterization with limited well control Oldenziel, T., Aminzadeh, F., de Groot, P. and Nielsen, S. (GeoInfo; dGB agent), 2002. EXPOGEF, 7th International SBGf in Bahia, Brasil
[36]Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia Ligtenberg, H. and Wansink, G. (formerly dGB), 2001.Journal of Petroleum Geology JPG, vol.24(4), October 2001, pp 389
[37]Prediction of static and dynamic parameters from time-lapse 3-D seismic Oldenziel, T., de Groot, P. and Kvamme, L. (formerly Statoil), 2000.62st EAGE Conference, Glasgow
[38]Estimation of Reservoir Properties by Monte Carlo Simulation Nakayama, K. (JGI; dGB agent), 2000. SPE Asia Pacific Conference on Integrated Modelling for Asset Management, Yokohama
[39]Neural network-based prediction of porosity and water saturation from time-lapse seismic; a case study Oldenziel, T., de Groot, P. and Kvamme, L. (formerly Statoil), 2000.First Break, February 2000.
[40]Volume Transformation by way of Neural Network Mapping de Groot, P., 1999. 61st EAGE Conference, Helsinki
[41]Seismic Reservoir Characterisation Using Artificial Neural Networks de Groot, P., 1999. 19th Mintrop seminar, Muenster, Germany
[42]Unsupervised segmentation in seismic data analysis Grennberg Fismen, B., Clausen, S., Yang, L., Carlin, M., Kavli, T. (all Sintef) and Wansink, G. (formerly dGB), 2001. ICIP conference in Thessaloniki, Greece
[43]A new confidence bound estimation method for neural networks, an application example Wansink, G. (formerly dGB), Yang, L. (Sintef), et al., 2001. 63rd EAGE conference, Amsterdam
[44]An evaluation of confidence bound estimation methods for neural networks Yang, L. (Sintef), et al., 2000. presented at ESIT
[45]Reservoir parameter estimation using a hybrid neural network Aminzadeh, F., et al., 2000. Computer and Geoscience
[46]Neural networks introduction A document from 1998 describing Multi-Layer-Perceptrons, Radial-Basis-Functions and Unsupervised-Vector-Quantiser.
[47]Stochastic inversion of seismic data by evaluation of pseudo-wells Bril, A. and de Groot, P., 1998. 60th EAGE conference, Leipzig
[48]Analysing and simulating stratigraphic patterns using Markov Chain analysis Bekkevold, J. and Bril, A., 1998 60th EAGE conference, Leipzig
[49]dGB-GDI Introduction A general introduction to dGB-GDI including the integration framework concept, the well simulator and neural networks.
[50]Monte Carlo statistics A document from 1997 on simulating correlated multi-variate stochastic variables. Be prepared for some heavy mathematics.
[51]Rock-physics relationships A document from 1997 explaining Gassmann's equation and some other basic equations used
in rock.
[52]The use of pseudo-wells in seismic interpretation studies de Groot, P. and Bril, A., 1996. 58th EAGE conference in Amsterdam
[53]Dealing with the geoscientific scaling problem - an Object-Oriented approach de Groot, P. and Bril, A., 1996. 1996 EAGE Winter Symposium
[54] Soft computing and intelligent data analysis in oil exploration Nikravesh, M., Aminzadeh, F. and Zadeh, L.A., ed., 2003. With contributions from Ligtenberg, H. and Wansink, A.G. and Tingdahl, K. Developments in pertoleum science series, vol. 51. Elsevier.
[55]Point bar geometry, connectivity and well test signatures de Rooij, M., Corbett, P. (Herriot-Watt) and Barens, L. (Total), 2002. First Break, volume 20
[56]Challenges direct future of geophysics Aminzadeh, F., 2000. Special millenium edition of the American Oil and Gas Journal
[57] Quantitative interpretation generally needs no seismic attributes de Groot, P. and Bril, A., 1997. 59th EAGE conference, Geneva
[58] Should we store all data in standardized data stores? Bril, A. and de Groot, P., 1996. 58th EAGE conference, Amsterdam