In this paper we propose a new rock physics workflow which uses a combination of the Hashin-Shtrikman bounds (Hashin and Shtrikman, 1962) and the Joint Self Consistent Approximation (Bruggeman, 1935; Landauer, 1952; Berryman, 1995) and Differential Effective Medium model (Bruggeman, 1935; Sen
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 202A (Anaheim Convention Center)
Presentation Type: Oral
We present an anisotropic rock physics model which can be used to estimate velocities for different facies types (sands, shales and carbonates). The model uses a combination of the joint Self Consistent, Approximation and Differential Effective Medium model (SCA/DEM) and the Hudson model for fractures. The SCA/DEM model is used to build the frame of the rock and the Hudson model adds fractures in 3 orthogonal directions with varying concentrations inducing anisotropy. Allowing the model parameters to change gives enough flexibility to the model to model different facies including sands and carbonates. The model has been tested against sand, shale and carbonate data from well logs in the Barents Sea and the North Sea. Anisotropy for this well was estimated using the method of White (1983). Results show a good fit between the rock model and the data.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 202A (Anaheim Convention Center)
Presentation Type: Oral
In this paper we propose a new workflow to perform Petrophysical Joint Inversion (PJI) of surface to surface seismic and Controlled Source ElectroMagnetic (CSEM) data, to recover reservoir properties (clay volume, porosity and saturation). Seismic and CSEM measurements provide independent physical measurements of subsurface that complement each other. In the case of well-logs, the basis of the PJI training dataset, taking advantage of such complementarity is straightforward. Indeed, elastic and electric measurements of earth properties sense the same earth volume at much the same scale. When applying the training dataset to the surface data derived geophysical attributes, the order of magnitude gap in between the scale at which those elastic and electric attributes represent the earth undermines dramatically PJI validity. Various CSEM inversion constraining methods (regularization breaks, prejudicing, use of an a priori model etc) help to reconcile seismic and CSEM resolution, but they are usually proven to be insufficient or inaccurate. In addition to these methods, we suggest adding a further downscaling step, so the recovered electric attribute resolution can be adequate with respect to the seismic one, hence fit for purpose. Such downscaling is designed to be consistent in electrical attribute space via transverse resistance within a rockphysics framework. The workflow will be demonstrated on a case study.
An integrated study of the well Zhao-104 and surrounding wide-azimuth 3D seismic 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. The 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. Seismic attribute analysis of anisotropy from elliptical velocity inversion indicates that anisotropy varies horizontally and vertically, and that it is dominantly controlled by stress azimuth, which conforms to the current day stress field as independently determined from borehole break-outs.
Seismic attribute analysis of anisotropy from elliptical velocity inversion indicates that anisotropy varies horizontally and vertically, and that it is dominantly controlled by stress azimuth, which conforms to the current day stress field as independently determined from borehole break-outs. For the reservoir, it appears that the modern– day SH (N40E) orientation approximates the conjugate fracture orientation of a wrench-faulted tectonic regime; this map pattern suggests a clockwise net rotation of the stress field from time of deposition to the present-day by 40°. Very large strike-slip faults (cutting the survey) have low anisotropy. Intermediate strike-slip faults cutting the entire shale section may exhibit larger anisotropy. Structural depressions formed by transtension act as TOC-rich sinks and likewise feature large anisotropy vectors. Relative paleo-sea-level change influenced mineral assemblages and elastic properties of systems tracts. Of several interpreted transgressions, only the first transgressive phase is associated with significant TOC-deposition.
At the end, the multi-attribute data fusion process to integrate all “sweet spot” parameters, such as shale formation depth, thickness, fault and fracture zone intensity and distribution, TOC and its thickness distribution, local stress field and its orientation, formation brittleness, pressure coefficient, impedance, Passion's ratio, Young's modules, porosity distribution, was used to predict “sweet spot” for shale gas reservoir exploration and production.
Presentation Date: Tuesday, September 26, 2017
Start Time: 2:40 PM
Presentation Type: ORAL
Most seismic data is processed to optimize image quality for structural and stratigraphic interpretation, with little regard to preserving characteristics essential for successful seismic reservoir characterization. No matter how sophisticated the inversion algorithm, use of inadequately processed seismic data will severely impact the quality of the final interpretation.
The objective of this paper is to quantify the advantages of performing pre-stack data conditioning prior to reservoir characterization. Three specific seismic properties that will be addressed are: (1) signal-to-noise ratio (SNR), (2) off set-dependent frequency loss, and (3) gather alignment.
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. In all of our integrated studies, our goal is to offer the greatest understanding of the depositional environment, so that decisions can be made with confidence.
Presentation Date: Tuesday, September 26, 2017
Start Time: 3:55 PM
Location: Exhibit Hall C/D
Presentation Type: POSTER
Alvarez, Pedro (RSI) | Marcy, Fanny (Engie) | Vrijlandt, Mark (Engie) | Nichols, Kim (RSI) | Keirstead, Rob (RSI) | Smith, Maggie (RSI) | Wen Tseng, Hung (RSI) | Bouchrara, Slim (RSI) | Bolivar, Francisco (RSI) | Rappke, Jochen (Engie) | MacGregor, Lucy (RSI)
We present a case study from the Hoop area of the Barents Sea, in which seismic, well log and controlled source electromagnetic (CSEM) data were integrated within a rock physics framework, to provide a robust assessment of the prospectivity of the area. Combining seismic and CSEM results can resolve the ambiguities that are present when only a single data type is considered. In this example, although seismic data identified potential hydrocarbon bearing sands, the saturation was uncertain. In this area and at shallow depth, the main focus is on (very) high oil saturations. Adding the CSEM data in this setting allows us to distinguish between high saturations (> 70%), and low and medium saturations (< 50%): it is clear that saturations similar to those observed at the nearby Wisting well (>90%) are not present in this area. However, because of limitations on the sensitivity/recoverability of the CSEM data in this high resistivity environment, it is not possible to distinguish between low and medium saturations. This remains an uncertainty in the analysis.
Presentation Date: Wednesday, September 27, 2017
Start Time: 3:30 PM
Presentation Type: ORAL
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in San Antonio, Texas, USA, 1-3 August 2016. 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.
Ellis, Michelle (RSI) | MacGregor, Lucy (RSI) | Ackermann, Rolf (RSI) | Newton, Paola (RSI) | Keirstead, Robert (RSI) | Rusic, Alberto (RSI) | Bouchrara, Slim (RSI) | Alvarez, Amanda Geck (RSI) | Zhou, Yijie (RSI) | Tseng, Hung-Wen (RSI)
In this study we use Controlled Source Electromagnetic (CSEM) data, well log data and rock physics to investigate electrical anisotropy drivers in the Snøhvit area of the Barents Sea. Results show that for the shale dominated sediments electrical anisotropy varies systematically with porosity, depth and elastic properties. However there is little systematic trend with clay content.
CSEM can be used to provide higher sensitivity to hydrocarbon saturation than is possible to achieve with conventional seismic reflection data (MacGregor & Tomlinson, 2014). In CSEM’s infancy anisotropy was ignored, however, disregarding resistivity anisotropy will lead to misleading CSEM survey feasibility studies, inaccurate CSEM data analysis, inaccurate estimations of hydrocarbon saturations and, consequently, erroneous interpretations (Ellis et al., 2011). In order to improve our interpretation of CSEM data we need to understand what drives the anisotropy for a given rock type. The aim of rock physics is to understand the relationship between geophysical observations and the underlying physical properties of the rock (Mavko et al., 2009). Physical properties include properties such as porosity, mineral composition, pore-fluid composition and sediment microstructure. By using rock physics we can start to understand the controls on electrical resistivity and anisotropy in a given area. The aim of this project is to determine the controls on electrical anisotropy in the Snohvit area of the Barents Sea and forms part of a wider study of Barents Sea electrical properties (Bouchrara et al, 2015). The Barents Sea was chosen as a study area because of the current interest in the area and the rich dataset which included well logs and CSEM surveys (Figure 1). Also the Barents Sea is geologically complex – stratigraphically, structurally, and historically (Gabrielsen et al., 1990). One component of this complexity is the presence of strong anisotropy in measured and derived electrical resistivity (Fanavoll et al., 2012).
Our study applied a geophysical well log analysis, rock physics diagnostics and rock physics modelling to an exploration well log data from a shale gas exploration area in the Sichuan Basin of South China. The study established an unconsolidated model (80% quartz plus 20% clay in the shale gas formation) transform between the acoustic and elastic impedance on the one hand and lithology, porosity, water saturation, clay content, quartz content, and TOC content on the other hand. Through our geophysical well log analysis, we calculated mineral volumes using best available data, total and effective porosity, water saturation, and bulk density and VS prediction where it was missing. For rock physics modeling, the shale gas formation matrix substitution (Clay, Quartz and TOC) and porosity modeling were performed in this exploration well. Crossplots are also used to analyze the elastic properties of the shale gas formation including VP velocity vs density, Acoustic Impedance (AI) vs total porosity (ΦT), AI vs Poisson’s Ratio (PR), and VP vs VS. The results were quality controlled by core sample laboratory analysis data. To understand seismic effect as a result of rock physics modeling, ray traced synthetic modelling has been applied. The Ray-traced synthetics have been generated for the in situ and modeled scenarios for AVA analysis. These transforms will be upscaled and applied to acoustic and elastic impedance inversion volumes to map lithology, porosity, and TOC distribution in the shale gas exploration area.
The characterization of shale gas formation is rapidly gaining prominence as a result of the increasingly important role of unconventional reservoirs in South China. Engineering practices such as horizontal drilling and hydraulic fracturing in shale gas formation has created a need for volumetric quantification not only of fracturing but of other geomechanical properties such as Young’s Modulus, Poisson’s Ratio, and maximum horizontal stress direction, in addition to more conventional reservoir properties such as lithology, brittleness, TOC and porosity as input for reservoir simulations
Summary It is the key for the success of the Paleozoic marine shale gas exploration and development in the South China to search for the sweet spots or shale gas-rich zones within the shale gas reservoir. Since it directly impacts the shale gas enrichment degree and hydro-fracturing effect of the shale gas reservoir, the development degree of shale gas reservoir fractures is the important factor for the prediction and evaluation of the sweet spots or shale gas-rich zones. We focus especially on using neural network technique to analyze the 3D seismic attribute data for fracture prediction. The examples show that using the wide-azimuth 3D seismic attribute technique could play an important role in the prediction and assessment of the gas-rich zones in shale gas reservoirs. Introduction In the exploration and production process of shale gas, the optimization of favorable shale gas areas, the prediction of sweet spots or shale gas-rich zones and the hydro-fracturing effect assessment are the three key stages.