Seismic and well log data integration using data-matching techniques

Relating well log data to seismic data is an important step in integrated reservoir characterization studies. Traditionally, an interpreter uses well log data, which has high vertical resolution but little lateral coverage, to understand amplitude variations in seismic data, which has lower vertical resolution than well logs but high spatial coverage. The process of calibration is referred to as a seismic-well tie. Several problems arise with the assumptions of conventional seismic-well tie workflows. The seismic-well tie involves generating a reflectivity series from available sonic and density logs acquired at the well, which inherently assumes all wells have a sonic and density log available along the entire length of the well. In many cases, this assumption is not valid as the number of wells drilled often out-numbers the number of sonic and density logs acquired. Common procedures to account for missing well logs in seismic-well ties are to use a time-depth relationship from a nearby well or use an empirical relationship to estimate the missing well log from an available well log. These methods provide constructive solutions. However, variations in structure, stratigraphy or missing/incomplete well logs can result in inaccurate seismic-well ties. In this thesis, I propose a method that predicts missing well log data by first estimating the shifts that align well logs with a reference type log. Once in this stratigraphically correlated, or `relative geologic time,' domain, I interpolate the missing well log data from available logs of the same type. The resulting well log is consistent with available well data and is not distorted by structural or stratigraphic variations. Once complete well log suites are estimated for each well, I focus on improving the efficiency and consistency of multiple seismic-well ties. The seismic-well tie typically involves a subjective and labor-intensive workflow that depends on the interpreter's experience and intuition. I introduce an automatic workflow using local similarity to match the synthetic with the real seismic trace. The advantage of using local similarity to compute the seismic-well tie is that consistent, repeatable, seismic-well ties are achieved. I generate a global log property volume by interpolating log data along local seismic structure and perform blind well tests to verify the accuracy and consistency of seismic-well ties. I apply this workflow to a 3D seismic dataset with 26 wells and achieve consistent, accurate and reproducible seismic-well ties. Combining the results of the well log interpolation and seismic-well tie I can generate a time-to-depth relationship for each well regardless of the initial well log suite. As a result, it is possible to generate log property volumes that integrate the high spatial coverage of seismic data with information from well log data. Well log data can also provide a useful source of information during velocity model building for depth migration. Using concepts and workflows described previously, I show that the mis-tie between a modeled synthetic and real seismic trace is related to an inaccurate migration velocity. Furthermore, this information can be used to update the migration velocity model such that modeled synthetic seismograms, the seismic image, migration velocities and well log velocities become consistent.Geological Science

Doctor of Philosophy

dissertationVisualization and exploration of volumetric datasets has been an active area of research for over two decades. During this period, volumetric datasets used by domain users have evolved from univariate to multivariate. The volume datasets are typically explored and classified via transfer function design and visualized using direct volume rendering. To improve classification results and to enable the exploration of multivariate volume datasets, multivariate transfer functions emerge. In this dissertation, we describe our research on multivariate transfer function design. To improve the classification of univariate volumes, various one-dimensional (1D) or two-dimensional (2D) transfer function spaces have been proposed; however, these methods work on only some datasets. We propose a novel transfer function method that provides better classifications by combining different transfer function spaces. Methods have been proposed for exploring multivariate simulations; however, these approaches are not suitable for complex real-world datasets and may be unintuitive for domain users. To this end, we propose a method based on user-selected samples in the spatial domain to make complex multivariate volume data visualization more accessible for domain users. However, this method still requires users to fine-tune transfer functions in parameter space transfer function widgets, which may not be familiar to them. We therefore propose GuideME, a novel slice-guided semiautomatic multivariate volume exploration approach. GuideME provides the user, an easy-to-use, slice-based user interface that suggests the feature boundaries and allows the user to select features via click and drag, and then an optimal transfer function is automatically generated by optimizing a response function. Throughout the exploration process, the user does not need to interact with the parameter views at all. Finally, real-world multivariate volume datasets are also usually of large size, which is larger than the GPU memory and even the main memory of standard work stations. We propose a ray-guided out-of-core, interactive volume rendering and efficient query method to support large and complex multivariate volumes on standard work stations

Seismic expression of shear zones: Insights from 2-D point-spread-function based convolution modelling

Shear zones are common strain localization structures in the middle and lower crust and play a major role during orogeny, transcurrent movements and rifting alike. Our understanding of crustal deformation depends on our ability to recognize and map shear zones in the subsurface, yet the exact signatures of shear zones in seismic reflection data are not well constrained. To advance our understanding, we simulate how three outcrop examples of shear zones (Holsnøy - Norway, Cap de Creus - Spain, Borborema - Brazil) would look in different types of seismic reflection data using 2-D point-spread-function (PSF)-based convolution modelling, where PSF is the elementary response of diffraction points in seismic imaging. We explore how geological properties (e.g. shear zone size and dip) and imaging effects (e.g. frequency, resolution, illumination) control the seismic signatures of shear zones. Our models show three consistent seismic characteristics of shear zones: (1) multiple, inclined reflections, (2) converging reflections, and (3) cross-cutting reflections that can help interpreters recognize these structures with confidence.publishedVersio

Sealing Potential of Shale Sequences through Seismic Anisotropy Analysis

This study investigates the potential relation of seismic anisotropy measured by surface seismic and the sealing potential of the shale sequences. Two case studies analyzed such a relationship. The Gippsland basin and Exmouth sub-basin are both hosts to prolific hydrocarbon resources and offer plenty of seismic data and sealing potential measurements. Weak anisotropy parameters extracted from carefully reprocessed seismic data show in both cases a positive correlation between sealing capacity and anisotropy of the shale

Double-difference waveform inversion: Feasibility and robustness study with pressure data

Time-lapse seismic data are widely used to monitor reservoir changes. Qualitative comparisons between baseline and monitor data sets or image volumes provide information about fluid and pressure effects within the reservoir during production. However, to perform real quantitative analysis of such reservoir changes, quantitative estimates of the elastic parameters are required as input parameters to rock-physics-based reservoir models. Full-waveform inversion has been proposed as a potential tool for retrieving subsurface properties, such as P- and S-wave velocities and density by fitting simulated waveforms to seismic data. An extension of this method to time-lapse applications seems straightforward, but, in fact, it requires more tailored processes such as double-difference waveform inversion (DDWI). We used realistic 2D synthetic pressure data examples to compare the performance of DDWI with that of two other inversion schemes: one using the same starting model for both inversions and the other starting the monitor inversion with the final baseline inversion model. The data simulation and inversion were based on acoustic theory. Although P-wave velocity changes were reliably recovered by each inversion method, DDWI was found to deliver the best results when perfectly repeated surveys were used. However, differencing the baseline and monitor data sets, as required by DDWI, could be found to be sensitive to the presence of survey nonrepeatability. To investigate the feasibility of using DDWI in practice, the dependence of DDWI on the quality of the baseline models and its robustness to survey nonrepeatability were studied with numerical tests. Various types of nonrepeatability were considered separately in the synthetic tests, including random noise, acquisition geometry mismatch, source wavelet discrepancy, and overburden velocity changes. A study of the correlation between the levels and types of nonrepeatability and the resulting contamination of the inversion results found that, for pressure data, DDWI was capable of inverting reliably for P-wave velocity changes under realistic survey nonrepeatability conditions