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Brazilian Pre-Salt reservoir characterization and monitoring is a challenge for surface geophysical methods due to the inherently minute observable signals from the reservoirs, located below the massive salt, in depth ranges > 5000 m.
The status of a program to develop a wireline deployed 3-axis gravity sensor with a target sensitivity of ~ 5 μGal is firstly introduced. This is followed by a feasibility study for the potential deployment as a time-lapse gravity survey (4D gravity monitoring) within the pre-salt. A conceptual pre-salt reservoir model for the Libra field, Santos Basin, the offshore of Brazil, is developed, built on available pre-salt knowledge provided from seismic imaging and well log data, including reservoir and production characteristics. The model is based on water substituting oil over 6 months within and through the reservoir, with adoption of an oil production rate for the field published in 2017 by the ANP. These data are used to forward model gravity to understand the potential for a detectable signal and thereby establish a baseline for a time-lapse gravity survey (4D gravity) that could be used to monitor the Libra Field.
A clear gravity response >80 μGal is observed over a six month period in the reservoir, due to water replacing oil, at the defined oil production rate. In a three-axis measurement the vertical axis is directly related to the magnitude of the fluid substitution and the two horizontal axes are sensitive to the fluid movement directions. Together, these suggest that an annual survey with a limited well stock could be effective in monitoring this type of reservoir and that a wireline deployed 3-axis gravity tool is likely to provide significant additional surveillance to constrain a reservoir production strategy through better appreciation of the direction of water movement. The follow-on step would be to model further field data and run a baseline survey to develop a novel reservoir surveillance method within the Pre-Salt.
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.
In 2016, Wintershall Norge AS and PL847 partners were awarded a production license covering blocks 6706/5 and 6706/6 of the Norwegian Continental Shelf. The area is located in the Vøring Basin on the Nagflar Dome, north of the Hel Graben. The Aasta Hansteen gas development is located approximately 60 kilometres to the South.
Four Upper Cretaceous prospects have been identified in addition to the Hvitveis discovery. Hvitveis is a faulted 4-way dip closure. Marisko, and the three other prospects are tilted fault blocks. The proven reservoir in Hvitveis and the targeted reservoir in the prospects are sands of the Upper Cretaceous Nise Fm level (Figure 1).
All recent discoveries at the Nyk High and Vema Dome show flat spots or amplitude anomalies related to hydrocarbon fill. In the Greater Aasta Hansteen area, the reservoir properties are generally good, with porosity values exceeding 30% and permeabilities in the Darcy range. However, in the Hvitveis discovery well 6706/6-1, the reservoir properties are much poorer with an average porosity of 15% and estimated permeability values ranging from very tight to 5mD. These poor reservoir properties could partly explain why no obvious flat-spot or hydrocarbon related amplitudes anomalies were observed in Hvitveis or in the four other prospects on the vintage seismic at the time of the application.
The initial part of the work program was a re-processing of the available 3D seismic to identify potential direct hydrocarbon indicators and allow a more detailed interpretation and understanding of reservoir distribution. To address these goals, a Pre-Stack Depth Migration was run using a dataset re-processed with the latest de-multiple and de-ghosting technology.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral
We carry out inversion of the marine controlled-source electromagnetic data using genetic algorithm to estimate the subsurface vertical resistivity. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the a-posteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on two synthetic data sets generated from two different horizontally stratified earth models. The first model had one thin resistive hydrocarbon layer between the low-resistive sediments, whereas the second model had multiple thin resistive layers. For both cases, our inversion estimated the resistivity to a reasonable accuracy. Additionally, we tested our method to invert the multi-frequency data which further improved the quality of the inverted results. The results obtained from this inversion can form a basis for higher dimensional modelling and inversions. Also, this method can be easily extended to implement the joint inversion using seismic data.
Presentation Date: Wednesday, September 27, 2017
Start Time: 2:40 PM
Location: Exhibit Hall C/D
Presentation Type: POSTER
Waveform-based inversions have been receiving a considerable attention over the recent years in the oil and gas industry. Going beyond the assumptions behind the amplitude-variation-with-offset/angle inversion and honoring complex effects of wave propagation, such waveform-based methods are effective in accurately delineating the subsurface reservoir properties. In this work, we develop a prestack waveform inversion method using multilevel parallelization and apply it on a real data volume from the Rock-Springs uplift, Wyoming, USA. We further use the inversion results to identify some key formations. Additionally, because the primary purpose of acquiring the Rock-Springs uplift seismic data was to characterize the subsurface for carbon dioxide sequestration, we also use our inversion results to analyze some potential target reservoirs and their associated seals. By demonstrating that our analysis is capable of producing a high-resolution image of the subsurface elastic earth properties, we conclude that prestack waveform inversion is an effective tool for reservoir characterization.
Presentation Date: Tuesday, October 18, 2016
Start Time: 10:45:00 AM
Presentation Type: ORAL
Estimation of reservoir rock and fluid properties is highly dependent on the parameters used in the rock physics models. Determining water saturation is very important to estimate hydrocarbon reserves and is often computed using Archie’s equation. Any uncertainty associated with the parameters in the Archie model as well as resistivity of clay in shaly sand formations may cause errors in the estimation of petrophysical parameters. It is very important to evaluate the relative impact of these parameters on reservoir rock and fluid properties. In this work, a series of sensitivity tests were carried out in order to investigate which of the formation water resistivity
Presentation Date: Wednesday, October 19, 2016
Start Time: 2:45:00 PM
Presentation Type: ORAL
Seismic reservoir characterization aims to provide an accurate reservoir description of rock and fluid properties estimated from seismic data. However, in several applications, seismic data only, cannot accurately discriminate the fluid effect, and the integration of other geophysical measurements, such as electromagnetic data, is required to improve the reservoir description. In this work, we propose a joint rock physics inversion to estimate porosity and fluid saturations from seismic velocity and electrical resistivity. The method is based on a Bayesian approach to inverse modeling and combines inverse theory and statistical rock physics relations. The advantages of this approach are the joint estimation of rock properties, achieved by a coupled rock physics model, and the estimation of the uncertainty associated to the predicted model, achieved through the Bayesian approach. The method has been applied to a real dataset, the Rock Spring Uplift field in Wyoming, a CO2 sequestration study.
The goal of seismic reservoir characterization is to provide a reliable model of the reservoir, in terms of rock properties, such as porosity and lithology, and fluid saturations. In rock physics models, when rock properties are known, we can predict the effect of fluid saturations on P-wave and S-wave velocity and density (Mavko et al., 2009; and Dvorkin et al., 2014). However, the solution of the inverse problem, i.e. the estimation of rock and fluid properties from velocities and density, is generally a challenging task (Avseth et al., 2005; and Doyen, 2007). Indeed, the solution of the inverse problem is not necessarily unique: two different rocks could have different porosities, lithologies and fluids, and the same elastic response. Furthermore, when the inverse problem is solved using seismic data instead of well log data, the low resolution and low signal-to-noise ratio of the data often increase the uncertainty in the estimation of seismic velocities and density, which makes the rock-fluid property estimation more challenging. To improve the reservoir description and reduce the associated uncertainty, we propose to integrate electromagnetic (EM) data, together with seismic attributes, in the reservoir modeling workflow (Du and MacGregor, 2010; MacGregor, 2012).
Horstmann, Mathias (Schlumberger) | Sun, Keli (Schlumberger) | Berger, Peter (OMV) | Olsen, Per Atle (Statoil) | Omeragic, Dzevat (Schlumberger) | Crary, Steve (Schlumberger) | Griffiths, Roger (Schlumberger) | Abubakar, Aria (Schlumberger)
Detection and quantification of formation resistivity anisotropy is important in many reservoirs, particularly those with laminations thinner than the axial resolution of standard measurements, when the properties of the individual layers cannot be resolved. Resistivity anisotropy evaluation is just as much important for background resistivity estimation in the overburden shales and underlying sequences where controlledsource electromagnetic (CSEM) surveys are acquired to reduce exploration risks. Formation dip determination is vital in almost all reservoirs.
Electromagnetic methods to determine the formation resistivity perpendicular to layering (Rv) and parallel to layering (Rh), in addition to the layering dip and azimuth, became available with triaxial induction measurements deployed on wireline (WL). Around the same time, deep directional electromagnetic (EM) measurements deployed on logging-while-drilling (LWD) tools ushered in the well placement revolution of remote detection of layers of differing conductivity.
With thousands of wells successfully positioned using the LWD directional EM technology, an alternate application for evaluation of resistivity anisotropy and formation dip is showing promise. Directional EM measurements are the LWD equivalent of wireline triaxial induction, also providing anisotropy measurements in vertical and deviated wells with enhanced sensitivity to formation dip decoupled from the anisotropy effect. Further, the LWD tool occupies a large portion of the borehole, displacing the mud and ensuring that the tool remains well centered, both of which reduce borehole effect in water-base mud. Finally, the availability of LWD directional EM while drilling provides the opportunity for thin-bed analysis and early decision making in difficult reservoirs.
The resistivity anisotropy and dip interpretation is based on a multistep inversion workflow optimized to take advantage of measurement sensitivities. In addition to resistivity anisotropy and formation dip evaluation in vertical and deviated wells, the technique provides quality control indicators based on inversion postprocessing.
Measurements and interpretation were successfully qualified in the Norwegian Barents Sea. Here the possibility of borehole integrity issues or its degradation over time increases the risk of having limited or no logging runs on WL. Also, planned riser-less pilot holes may allow data acquisition only while drilling. In these situations LWD resistivity anisotropy data must provide the kernel for interpretation.
The acquired resistivity anisotropy can be used as input for building CSEM models, reduce resistivity anisotropy uncertainties and support defining constraints for the 3D deep EM inversion-based interpretation. Field testing has provided good opportunities to enhance the measurements and validated the service. With implementation of improvements, an agreement with WL triaxial induction, in a range of different environments, is demonstrated. Several field test examples have proven that resistivity anisotropy and dip evaluation from LWD directional EM measurements is a viable alternative to the traditional WL service.
Many oilfields are in the development stage at present, it has important significance to monitor oil and gas reservoirs using time-lapse electromagnetic. Four continuous electromagnetic profiling (CEMP) lines with 199 stations have been acquired in the Sebei gas field. Remote reference is used to suppress noise during field data acquisition. Data format of 4 frequencies per octave is output to improve the resolution and information of the filed data. High precision two-dimensional magnetotelluric (MT) inversion is adopted to process the data. The inversion results show that, the resolution has been improved with high sampling rate of CEMP data and high precision processing method. It provides technical support for time-lapse MT monitoring. The high resistance characteristic is very clear shown in the inversion profiles of Sebei gas filed. It provides important basis for time-lapse MT monitoring oil and gas reservoir. The inversion results are consistent with the logging curves, and the predicted reservoir area and thickness are consistent with the known wells and the collected data.
Nivlet, Philippe (EMGS Services México) | Sánchez Pérez, Luis (EMGS Services México) | Escalera Alcocer, José Antonio (Pemex subdirección Exploración) | Trinidad Martínez Vázquez, José (Pemex subdirección Exploración) | Herrera, Arturo Escamilla (Pemex subdirección Exploración) | Vázquez García, Marco (Pemex subdirección Exploración) | Salazar Soto, Humberto (Pemex subdirección Exploración)
This case study presents results from the Orion project within the Gulf of Mexico’s Lloyd Ridge area. Difficulties arose in updating the velocity within the Cotton Valley Formation (CVF), a thin low-reflectivity layer of shale with carbonate stringers. The prestack depth migration (PSDM) common-image gathers (CIGs) and stack displayed noticeable undulations. To reduce the undulations, an offset-dependent residual moveout (RMO) picking technique was combined with high-resolution tomographic inversion to update the velocity model. The combined work flow resulted in a higher resolution velocity model, which enhanced the gather flatness and reduced the undulations of the event beneath the CVF layer.