Uwe Strecker - Academia.edu (original) (raw)

Papers by Uwe Strecker

ABSTRACT Seismic interpreters are required to work with larger and larger seismic volumes as the ... more ABSTRACT Seismic interpreters are required to work with larger and larger seismic volumes as the amount of seismic data we acquire and process continues to increase. Rapid advances in seismic attribute methods further increase our data-set sizes by providing many coincident seismic attribute volumes for each data set. These exponential increases in available data represent huge data management and data interpretation challenges to our industry. There are clear similarities between the seismic exploration industry and the Internet in terms of the volume of information that is available for analysis, and therefore it makes sense to deploy data mining tools and methodologies developed for other industries to address the needs of the oil and gas exploration business. Here we employ some aspects of the data mining workflow to enrich and discover knowledge about possibly productive regions within a 3-D seismic data volume from South Louisiana. Seismic data mining is applied to multiple seismic attribute volumes calculated from a 3-D dataset acquired for the Lake Theriot area, Terrebonne Parish, South Louisiana. Various instantaneous and geometric seismic attributes are used during data reduction for the rapid delineation of a possibly prospective, faulted subsurface channel system and the seismic properties of its sediment fill. Additionally, selected seismic attributes of the propagated wave field can be recombined mathematically to produce an algorithm that encapsulates geophysically descriptive aspects of subsurface seismic facies. Seismic attributes including variance of angle, time variance of instantaneous frequency, and variance of similarity, combine to form the "Shale Indicator". This is a "hybrid" seismic attribute that integrates certain depositional characteristics of shales, such as lateral continuity, thin-bed layering, and parallelism of bedding, in an effort to seismically differentiate "seismic shales" from "seismic non-shales." Additionally, application of neural network technology generates a single attribute volume of the multi-attribute response illuminating discrete seismic facies. The results of this study demonstrate the value of applying data mining techniques to seismic data volumes to rapidly establish zone prospectivity, thereby mitigating future drilling risk. End_of_Record - Last_Page 21--------

First Break, Jul 1, 2017

An integrated study of the well Zhao-104 and surrounding wide-azimuth 3D seismic data volume with... more An integrated study of the well Zhao-104 and surrounding wide-azimuth 3D seismic data volume within the shale gas reservoir in South China has been conducted with the objective of generating shale formation properties related to fracture orientation and intensity in the area and deriving such reservoir rock properties as data quality allows. Well data, structural seismic information and prestack inversion products were combined in an integrated interpretation. Seismic gather conditioning improved seismic data quality prior to pre-stack inversion by improving signal/noise ratio, removing NMO stretch and aligning reflection events. Velocities from residual moveout (RMO) analysis on individual sectors were used as input to detection of fracture orientation and anisotropy. Fracture strike and P-wave anisotropy were calculated using the RMO updated sector velocity fields in elliptical velocity inversion, while inversion for P and S impedance and derivative attributes produced volumes that relate to rock properties such as brittleness and rigidity that are likely to impact fracturing. Measured logging curves were edited and missing curves estimated for the entire wellbore for geophysical purposes. Porosity, mineralogy and saturation were also estimated and elastic attributes were examined in crossplot space to find discrimination in properties of interest. Matrix modelling and synthetic seismograms were studied in order to understand likely seismic signatures and AVA behaviour. A set of post-stack volumetric attributes that are indicative of the presence of faults and fractures were derived and fed into an unsupervised neural network to perform fracture facies classification. In addition, seismically resolvable faults and discontinuities were automatically generated from fault sensitive attributes. At the end, information from all parts of the project were combined to assess structural characteristics and identify areas of high fracturing and stress direction that are important for the placement of horizontal wells and likely high total organic content (TOC) zones, necessary as a source of hydrocarbons or ‘sweet spot’.

The Leading Edge, 2019

An analytical comparison of seismic inversion with several multivariate predictive techniques is ... more An analytical comparison of seismic inversion with several multivariate predictive techniques is made. Statistical data reduction techniques are examined that incorporate various machine learning algorithms, such as linear regression, alternating conditional expectation regression, random forest, and neural network. Seismic and well-log data are combined to estimate petrophysical or petroelastic properties, like bulk density. Currently, spatial distribution and estimation of reservoir properties is leveraged by inverting 3D seismic data calibrated to elastic properties (VP, VS, and bulk density) obtained from well-log data. Most commercial seismic inversions are based on linear convolution, i.e., one-dimensional models that involve a simplified plane-parallel medium. However, in cases that are geophysically more complex, such as fractured and/or fluid-rich layers, the conventional straightforward prediction relationship breaks down. This is because linear convolution operators no lo...

First Break, Apr 1, 2020

Abstract A new machine-learning approach based on a Dynamic Time Warping (DTW) algorithm is intro... more Abstract A new machine-learning approach based on a Dynamic Time Warping (DTW) algorithm is introduced to detect faults and fractures in 3D seismic data from an unconventional resource play in Eastern Cis- Caucasia (Russia). This novel approach allows for better edge detection in seismic amplitude volumes because it employs a detailed comparison of two neighbouring traces to detect discontinuity via a minimal horizontal distance. For benchmarking purposes the proposed DTW method is compared to a widely used multi-trace attribute (Variance). Subsequently, both calculated attribute cubes serve as an input for ANT-Tracking to delineate fault strike and fracture corridor trends. A comparison of results shows that better resolution and more complete fault images are obtained when the DTW method is applied.

Integrated analysis of well, seismic and controlled source electromagnetic (CSEM) data is key in ... more Integrated analysis of well, seismic and controlled source electromagnetic (CSEM) data is key in order to provide valuable information on reservoir properties and content. The purpose of this study was to jointly analyze well data, seismic data and CSEM data to provide a clear appraisal of a prospect offshore West Africa. Firstly, CSEM data was modeled and inverted. Results were then integrated with the available seismic and well data in order to better understand the resistivity changes we were seeing in the CSEM inversion. In particular, the observation of a low resistivity zone coincident with the seismically mapped prospect is disappointing; however two plausible geological explanations were considered and led to the decision of dropping the block.

SPG/SEG 2016 International Geophysical Conference, Beijing, China, 20-22 April 2016, Apr 22, 2016

The URTeC Technical Program Committee accepted this presentation on the basis of information cont... more The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper without the written consent of URTeC is prohibited.

First Break, Feb 1, 2019

Seismic trace segments in a 3D volume over a reservoir interval are classified into seismic facie... more Seismic trace segments in a 3D volume over a reservoir interval are classified into seismic facies units via neural network trace analysis. Unsupervised classification is carried out in two stages: 1) Training, whereby typical (average) objects of each class are estimated and 2) Classification stage whereby all study objects are assigned to a certain class, based on a minimum similarity to a typical object of this class. Input parameters for the algorithm are: the number of classes, the size of the vertical segment, the investigated time window and the colour scheme applied. Unsupervised classification is fairly rapid and several software packages are available for this purpose. In contrast, a supervised workflow is more demanding yet facilitates interpretation of results. In addition, supervised classification and calibration permit probabilistic uncertainty analysis. An example of a non-supervised classification scheme is shown and the main advantages of supervised partitioning are discussed.

First Break, Apr 1, 2012

Ran 4 and Gang Yu 4 argue that a significantly more robust interpretation of rock and fluid prope... more Ran 4 and Gang Yu 4 argue that a significantly more robust interpretation of rock and fluid properties for a pre-drill prospect appraisal can be obtained from the resistivity information acquired by a controlled source electromagnetic (CSEM) survey if seismic and well information are incorporated. This is illustrated using a case study from offshore West Africa, where a CSEM dataset was acquired in 2009.

First Break, 2020

EAGE/ALNAFT Geoscience Workshop, 2019

Proceedings, 2018

Summary Integrated seismic reservoir characterization of carbonate rocks potentially thwarts diff... more Summary Integrated seismic reservoir characterization of carbonate rocks potentially thwarts difficulties arising from reservoir heterogeneity owed to a complex geological history. The featured interdisciplinary workflow reconciles geological, geophysical and engineering components to address reservoir complexity in terms of stratigraphic architecture coupled with a distribution of layer properties that honors flow zonation. Primarily, this workflow calls for a combination of hydraulic flow unit definition embedded in sequence stratigraphy and is further augmented by seismic attribute analysis (i.e., seismic inversion, frequency decomposition of amplitude, etc.), rock physics, and geostatistical techniques to characterize an UAE onshore oil reservoir located within a Lower Cretaceous carbonate sequence (i.e., lower member of Shu´aiba Formation; Strohmenger et al., 2010 ). Using the aforementioned data, we construct an acoustic impedance model and proceed to invert the data in a deterministic and stochastic inversion. Using rock physics, we generate a probability cube of the reservoir properties distribution in a very complex carbonate setting.

First EAGE Conference on Seismic Inversion

SEG Technical Program Expanded Abstracts 2005, 2005

Seismic detection of hydrocarbons is fraught with ambiguity. Not only are simple methods such as ... more Seismic detection of hydrocarbons is fraught with ambiguity. Not only are simple methods such as seismic amplitude anomaly mapping often uncertain, but also anomaly interpretation, such as AVO analysis, often prove ambiguous: Are always the seismic amplitude and AVO signatures responding to changing fluid type or perhaps they can be affected merely by lithological heterogeneity? In this paper, we examine two wells drilled on amplitude, both were associated with AVO anomalies, but the wells had mixed results. In one case gas was found, however, in another, despite the prominent AVO anomaly, no reservoir sand was found. This mixed result puts in question the meaning and value of AVO and whether and how AVO can be used in direct hydrocarbon detection. In the case study under examination, both elastic and inelastic analysis (attenuation) of the data has been conducted. Based on the modeling results, a strategy for using hybrid attributes that include both the elastic and inelastic seismic attributes was devised to reduce the exploration risk associated with drilling other AVO anomalies present in the prospect area.