integration of seismic and electromagnetic measurements · seismic, electromagnetic and gravity...

Integration of Seismic and Electromagnetic Measurements Workshop SEG International Exhibition and 76th Annual Meeting, 1-6 October 2006, New Orleans

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Integration of Seismic and Electromagnetic Measurements

Thursday afternoon, 5 October 2006 Recently, electromagnetic methods such as controlled-source EM (CSEM) and magnetotelluric (MT) have received more attention by the exploration industry, particularly in the marine environment. While EM methods show merits of their own, their real strength lies in integration with seismic and other methods. The various geophysical measurements are sensitive to different properties of subsurface formations, and they have different resolution properties. In this workshop we will look at what we can gain by acquiring, processing, and interpreting these measurements together. While we would like to emphasize seismic methods, we will not exclude good case histories of integrating with gravity/magnetics.

Organizers: Cengiz Esmersoy, Norman Allegar, & Kurt Strack


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1:30 Introduction

Cengiz Esmersoy (Schlumberger) 1:35 Overview: Combined electromagnetic, gravity and seismic methods: the

added value of integration. Paolo Dell'Aversana (ENI)

1:55 Strategies for Joint Inversion of Geophysical Data.

Marion Jegen (IFM-GEOMAR) 2:15 EM: A Complementary Measurement not a Solution.

Ransom Reddig (AGO) 2:35 Remote determination of reservoir properties from joint interpretation of

electromagnetic, seismic and well log data. Lucy MacGregor (OHM), Peter Harris (Rock Solid Images)

2:55 Break (15 min) 3:10 Geophysical modeling through simultaneous Joint Inversion of Seismic,

Gravity and Magnetotelluric data. Michele De Stefano, Daniele Colombo (Geosystem)

3:30 Integrating processing and interpretation of EM and seismic data an

effective means for complex hydrocarbon objective. He Zhanxiang (BGP)

3:50 The challenge of subsurface resistivity unlocking: An example from a

recently drilled MCSEM survey. Tage Rosten (Statoil)

4:10 Integration of electromagnetic and seismic data to assess residual gas risk

in the toe-thrust belt of deepwater Niger Delta. Jochen Moser (Shell)

4:30 Open discussion/panel (30 min) 5:00 End


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Combined electromagnetic, gravity and seismic methods: the added value of integration Paolo Dell’Aversana, Eni E&P Every geological system can be characterized at least in terms of elastic, electric, magnetic and electromagnetic parameters. Seismic applications represent only one of the geophysical methods. If appropriately integrated, one can extract a multi-parametric physical response. Fundamental is the integration concept itself and how to set the optimal integration strategy. This problem is of technical and managerial nature. Especially in complex geological settings a more comprehensive geophysical approach should be used by the exploration managers. Every multi-parametric geophysical model can be considered as complex system. Complexity is generated by multi-dimensionality and by intrinsic non-linearity of the involved processes. We expect that new global properties can emerge from integration of independent data sets. These properties are from the synergy, which means they cannot be found in any one of the single geophysical interpretation. Inversion of electromagnetic data constrained by seismic boundaries can provide accurate reservoir parameters that would have been impossible for an electromagnetic method alone (Dell’Aversana, 2005). On the other side no seismic methodology exists that produces conductivity information. It emerges after integration with electromagnetic data. This highlights the importance of integration based on constrained (and/or joint) inversion of electromagnetic and seismic data. During the last two decades Eni acquired many seismic and non-seismic data sets simultaneously in the same exploration areas. (Buia et Al., 2002; Dell’Aversana, 2005). Several thousands of magnetotelluric (MT) soundings have been acquired in thrust belt environment, where seismic methods alone revealed their limitation in providing satisfactory imaging. Also geoelectric data have been collected aimed at improving the solution for static corrections of seismic data. During the last ten years, several Marine MT data sets have been acquired together with 2D and 3D seismic surveys. 2D and 3D Controlled Source EM (CSEM) surveys in both time (on land) and frequency domain (offshore) round off our portfolio. With so many independent data sets in the same areas, the problem of a proper integration must be solved, to obtain added value. Our approaches of geophysical integration are based on innovative procedures of joint, cooperative and constrained inversion. We demonstrate how seismic, electromagnetic and gravity data can be modelled and inverted for producing consistent multi-parametric models. At the same time proper integration generates an optimal velocity fields for improving seismic processing and imaging in depth. The simplest level of integration can be performed using the sharp-boundary information, as provided by seismic data, for constraining the inversion of electromagnetic and/or gravity data. For instance the interpreted seismic horizons on pre-stack depth migrated sections can be used as sharp constraints and carried in the electromagnetic (and/or gravity) domain. When using cooperative inversion we see a more circular nature. In this case the output model of one inversion process is transformed in the starting model for an inversion in a different


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geophysical domain (for instance, seismic velocities obtained by travel times tomography can be transformed into resistivity values using empirical relationships). Then the process continues by re-transforming the output model of the second inversion into an improved starting model for a new inversion in the first geophysical domain. This cycle can be continued until a stationary condition is reached. The most complex way for integrating different data set is joint inversion. In this case a composite cost function including the misfit between observed and predicted values for two or more independent data sets belonging to different domains (seismic, electromagnetic, gravity) is minimized. The different data sets have in general different weight, different resolution and different accuracy in the same joint-minimisation problem. This model should be also that one that produces the best seismic imaging. References

Buia, M., Baldino, F., Morandi, S., [2002], Thrust belts exploration - Limits of the conventional approach and recent innovation efforts. The ESIT project: aims and results. Extended abstract EAGE Florence 2002. Dell’Aversana, P.; Integration loop of Global Offset seismic, Continuous Profiling

Magnetotelluric and gravity data. First Break, Volume 21, 32-41, November 2003. Dell’Aversana P.; The importance of using geometrical constraints in Marine Controlled Source Electromagnetic data inversion. SEG 2005, Houston, SEG Technical Program Expanded Abstracts -- 2005 -- pp. 605-608.

Strategies for Joint Inversion of Geophysical Data Marion Jegen, IFM-GEOMAR

Inversion of a single type of discrete geophysical data is inherently non-unique due to resolution issues, choice of model parameterisation or physical equivalences in the response such that a variety of earth models may fit the data equally well. The degree of non-uniqueness is typically smallest for seismic data and largest for potential field data such as gravity data. The use of various datasets that contain complementary information can address this non-uniqueness issue but the problem now arises on how to best synthesize the information. Simple comparison of models derived from various methods looking for common features may be misleading since the model derived may only partially represent the true model due to the non-uniqueness of the response. Also how can one most efficiently combine the complementary information content in different data?

The answer is to jointly invert the geophysical data. Joint inversion of data measuring the same physical property has previously been done with

success. Vozoff and Jupp (1975) developed a scheme to jointly invert DC resistivity and EM data. Also seismic data using both reflected and refracted energy have been jointly inverted to constrain velocity structures (Holbro et al., 2003; Trinks et al., 2005). However, these approaches were limited to deriving one physical property of the subsurface e.g. the resistivity or velocity structure. It improves the determination of the particular property, but it does not benefit from


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estimating various physical properties which are highlighting different features of the geological structure.

Several promising attempts have been made to invert data sets measuring different physical properties. These attempts have been focused on finding a common structural feature in the model (Hering et al., 1995; Haber and Oldenburg, 1997; Gallardo and Meju, 2004; Musil et al., 2003). Looking for common structural features in the models improves the data interpretation considerably, however it still does not exhaust the information content of the data, since it neglects the fact that different data sets may resolve different structures.

Another possibility to capitalise on the different strengths of the various datasets is to define individual structures in one common model (Jegen and Hobbs, 2005; Heincke, Jegen and Hobbs 2006). This approach uses rock property relationships from borehole data in the region, such that a common model may be expressed in resistivity, velocity and density. This type of inversion is particularly useful for areas, where complementary information content in different geophysical data sets is particularly pronounced (e.g, Sub basalt and sub salt problems). With this approach, the entire structure of a model may derived, where single method inversion failed. References Gallardo, L.A., and Meju, M.A., 2004. Joint 2D resistivity and seismic inversion with cross-

gradients criterion. J. Geophys. Res., 109, 03311. Haber, E., and Oldenburg, D., 1997. Joint inversion: a structural approach. Inverse problems, 13,

63-77. Hering, A., Misiek, R., Gyulai, A., Ormos, T., Dobroka, M., Dresen, L., 1995. A joint inversion

algorithm to process geoelectricand surface wave seismic data. Geophysical Prospecting, 43, 135-156.

Jegen M., and R.W. Hobbs, 2005. “Outline of a Joint Inversion of Gravity, MT and Seismic Data”, Faroe Islands Exploration Conference: Proceedings of the 1st Conference. Annales Societas Scientarum Faeroensis, Supplementum 43, Torshavn, 2005.

Jegen M., Hobbs R.W., and Trinks, I. 2005., “Sub-basalt Imaging with Joint Inversion of Gravity, MT and Seismic Data”, EAGE, Madrid.

Hobbs, R., and Trinks, I., 2005. Gravity Modelling Based on Small Cells, 67th Mtg.: Eur. Assn. Geosci. Eng., P347.

Holbro, J., Singh, S. C., and Minshull, T. A., 2003. Three-dimensional tomographic inversion of combined reflection and refraction seismic traveltime data. Geophys. J. Int., 152, 79-93.

Jupp, D. L. V., and Vozoff, K., 1975. Stable iteration method for inversion of geophysical data, Geophys. J. Roy. Astr. Soc., 42, 957–976.

Musil, M., Maurer, H.R., and Green, A.G., 2003. Discrete tomography and joint inversion for loosely connected or unconnected physical properties: application to crosshole seismic and georadar data sets, Geophys. J. Intern., 153, 389-402.

Trinks, I., Singh, S. C., Chapman, C. H., Barton, P. J., Bosch, M., and Cherrett, A., 2005. Adaptive traveltime tomography of densely sampled seismic data. Geophys. J. Int., 160, 925-938.

Vozoff K, and Jupp DLB (1975) Joint inversion of geophysical data, Geophys. J. R. astr. Soc., 42: 977-991.


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EM: A Complementary Measurement not a Solution Ransom Reddig, AGO

The progressive move into deepwater exploration and smaller exploration targets brings with

it new levels of technical and economic risks making exploration decisions more critical than ever. As new measurements become available, it is important to understand that such measurements must be interpreted in the context of augmenting knowledge and improving understanding, and not as being solutions or answers in their own right. In this talk we examine the role EM technology can play in this context and explore two integrated approaches to highlight the power of integration with other sources of data. Remote determination of reservoir properties from joint interpretation of electromagnetic, seismic and well log data Lucy MacGregor, OHM Ltd, The Technology Centre, Claymore Drive, Aberdeen Peter Harris, Rock Solid Images, Gaustadalleen 21, 0349 Oslo, Norway The problem of remote characterization of reservoir properties is of significant economic importance to the hydrocarbon industry. In exploration the ability to determine the gas saturation in an identified prospect prior to drilling would avoid the costly drilling of un-economic low saturation accumulations. During development and production, a detailed knowledge of the reservoir properties and geometry, and changes in these parameters through time, can aid optimization of well placement to enhance overall recovery rates. A range of geophysical techniques can be applied to this problem. Seismic data are commonly used to develop geological models of structure and stratigraphy. Seismic data may also be used to constrain reservoir properties such as elastic impedance, porosity and fluid content. However seismic data alone in many situations cannot give a complete picture of the reservoir. Ambiguities exist, for example, in AVO responses which may be caused either by fluid or lithological variations, and some other source of information is required to distinguish them. In contrast controlled source electromagnetic (CSEM) methods provide a means of remotely determining resistivity structure within the Earth. CSEM data are particularly sensitive to the fluid properties and distribution within a reservoir and the surrounding strata. However due to the diffusive nature of electromagnetic fields in the earth, the structural resolution is generally lower than that given by seismic data. It is clear then that a careful combination of seismic and CSEM data, exploiting the strengths of each, can supply information which is not available or is unreliable from either type of data alone, thus reducing ambiguity and risk. Here we demonstrate this using seismic and CSEM data collected on the Nuggets-1 gas reservoir in the North Sea. Key to combining these complementary sources of information is the development of a common rock physics model linking both electromagnetic and elastic properties to the underlying rock and


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fluid properties. There are several approaches to this. For example the Faust Alternative can be used to predict Vp from resistivity. This was developed by researchers at Stanford for shales rather than sands (as the original Faust equation). At the Nuggets-1 well, a comparison of measured Vp in the well and the prediction from resistivity shows distinct linear sand and shale trends. The gas sands are well-separated since the derivation of the Faust Alternative assumes brine saturation. Firstly we inverted the CSEM data to give a cross section of electrical resistivity with depth across the field, which identified a resistive zone caused by the presence of the gas reservoir. Although the vertical resolution of the reservoir is lower than that achievable with seismic data, its lateral extent is resolved well. We then performed an inversion to acoustic impedance (AI) using the stacked seismic data. A crossplot of relative AI at the well trace with the resistivity resulting from inversion of the CSEM data at the location of the well, colour coded by well log resistivity, shows that the gas sand has low AI and high resistivity in the surface data, and that using the surface data the gas sand can be well delimited. Given this correlation at the well the surface seismic and EM data were used to map gas saturation in the reservoir across the lateral extent of the field. The end result of this process is semi-quantitative; we can distinguish areas of high and low gas saturation but the estimated values may be unreliable. Refinements of this methodology will allow more accurate determination of reservoir properties and, most importantly, associate uncertainties with the estimated reservoir properties. Combining CSEM, surface seismic and well data through a consistent rock physics model thus offers a powerful tool for risk reduction.


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Integrating processing and interpretation of EM and seismic data - an effective means for complex hydrocarbon objective He Zhanxiang, BGP on sabbatical at KMS Technologies and University of Houston

A new trend, integrating processing and interpretation of EM and seismic data for complex

hydrocarbon objective, appears presently in China. Especially, EM technique can significantly enhance traditional seismic effect. It is well known that seismic is not an effective means for the exploration of volcanic and rock salt area and sharp structures, and it is very difficult to judge whether a trap be hydrocarbon-bearing or not and map its boundary (especially for stratigraphic or lithologic traps) only based on seismic data. In recent years, EM technique is significantly and rapidly developed. Not only meter precision is highly improved, also a series of new techniques and processing and interpretation ways are developed. Thereinto, the typical techniques are CEMP (Continual EM Profile), high power TEM (Transient EM), high power SIP (Spectral Induced Polarization), high power TFEM (Time and Frequency EM), and borehole surface EM, and the typical processing and interpretation ways are joint inversion, constrain inversion and integrated interpretation. Those new techniques can be enhanced by integration with seismic, logging and other data and on the other hand they can improve seismic effect. The paper presents some applications recently in China by which we can give a general view to their development and application effect.

1. Integration of CEMP and seismic to identify volcanic rock Some structures were discovered at ZTD in Tarim Basin of China based on seismic data but

there is no any interpretation about their formation yet. Igneous rock is penetrated in target formation. It means that these structures be caused by basement magma flooding upwards into Permian formation, The structures are 3000m to 3500m deep and 200m to 600m thick. To map the boundary of igneous rock, CEMP and high precision geomagnetism surveys are deployed. Based on geomagnetism data the boundary of igneous rock is approximately mapped and based on CEMP data its space distribution is further inferred. During data processing, initial model is constructed based on seismic shallow information and interfaces. Then perform relax constrain inversion to obtain resistivity profile. Calculating second derivative of resistivity field can remove the background to describe resistivity anomaly. On second derivative profile, the target is clearly exhibited as a false structure. The profile provides important information regarding to the distribution of Permian igneous rock. With the reference of CEMP data, seismic data is re-processed and re-interpreted. As a result, its effect is significantly enhanced.

2. Integration of high power LoTEM and seismic to delimit igneous reservoir and non-

reservoir. The study area is located in north Tarim Basin where igneous rock is widely developed and some oil gas fields are discovered. Exploration experience indicates that igneous rock is one of major factors which can influence the identification of low amplitude structures in the area. In the area, several igneous-associated traps and reservoirs are discovered. Therefore, to study the development and distribution of igneous rock is important. Since igneous rock in the area is hard to recognize for its complex lithology and facies and it exhibits complex features on seismic sections, TEM is deployed. We perform inversion to EM data constrained by seismic structures, thus we can understand the distribution of high resistivity zones which may indicate the distribution of igneous rock. In addition, IP data is abstracted from observed magnetic field, thus polarizability anomaly sourced from deep reservoir beds can be mapped. Based on resistivity and polarizability anomalies, we can judge the objective be igneous rock or hydrocarbon structure which is an important basis for well deployment. Based on the anomalies, four wells have been


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designed and three have revealed commercial discovery and the other one is being penetrating presently.

3. Integration of high power SIP and seismic to identify lithology trap at well JN area Well JN is located on the central and northern parts of Tarim Basin, west China. Tertiary

reservoir beds are 4000m to 4500m deep. Several wells have shown commercial discoveries in reservoir beds, and there are also failure wells. To improve the reliability of inferred lithology reservoirs, SIP survey is designed with the reference of seismic lithology interpretation. Based on high power SIP data, three favorable anomaly zones and four favorable anomalies are inferred. These anomaly zones and anomalies can correspond to seismic intense anomalies in different time or frequency windows, and can provide important information for integrated interpretation. Cross-well SIP profiles match well with seismic sections regarding to inferred favorable objectives or zones and they also match well with known well information. Presently penetrated well J1-2 locating out of favorable zone is verified to be dry. After 500m subsequent horizontal penetration toward favorable zone, oil appears in the well.

4. Integration of high power TFEM and seismic to study lithology traps at TKQ area The study area is located in Qaidam Basin, west China. The field has been put into production

since 1960s’ for 40 years more. The major producing zone is coming into its terminal phase. Thus the expansion of producing area and new reservoir search become an urgent task. Nothing is discovered based on 2D and 3D seismic data. After 2003, 3D seismic survey was deployed to search for lithology reservoirs. Lithology reservoir research came into a new development phase from then on. In the area, different contractors provided different interpretation about reservoir boundary although all worked based on seismic data. Some wells succeeded and some failed. It means that there may be many lithology traps in the area but they are complex, and it is difficult to delimit oil-bearing traps only based on 3D seismic data. Under the situation, newly developed TFEM is deployed to identify commercial oil traps integrated with seismic data and consequently to improve well succeed rate. TFEM uses known seismic data as a restriction in data processing. Formation fluctuation on inversion profiles can match well with that on seismic sections. In addition, some high resitivity blocks appear on inversion profiles. On the other hand, resistivity anomaly and polarization phase difference anomaly are calculated from electric field and polarization anomaly also be obtained from electric and magnetic fields. These anomalies enhance the interpretation effect. Up to now, inferred result has been verified by a well.

5. Integration of borehole surface EM and seismic to delimit reservoir beds For borehole surface EM system, the source is located in well near reservoir bed and recorders

on the ground. .The system is convenient and efficient, its study range is larger than traditional logging, and it has higher resolution than common surface EM techniques. Therefore, borehole surface EM is one of practical and efficient ways presently for reservoir exploitation. The technique has been successfully applied in ten more areas of China and the succeed rate is at least 80%. A basic principle for the technique is the integration with seismic and logging data. Firstly, construct initial model and perform 2D inversion to the model with the reservoir depth and interfaces constrained by logging data and seismic data respectively. Then, compare the inversion resistivity anomaly with seismic-based hydrocarbon zone to get the first understanding about hydrocarbon distribution. The second is the information about reservoir resistivity and velocity anomalies. Experience indicates that stratigraphy velocity is positively correlated with resistivity in sedimentary basin but the former will decrease and the later increase when formation is contaminated by oil. After normalization, the oil-contaminated reservoir bed will show anomaly and vice versa. The magnitude of the anomaly is positively correlated with the oil-bearing probability. The third is that about IP data which can be obtained by borehole surface EM


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technique. IP data is another important reference for judging the hydrocarbon-bearing property of a reservoir. In the paper, several cases will be presented to show its effect.

The challenge of subsurface resistivity unlocking: An example from a recently drilled MCSEM survey Tage Rosten (Statoil) (Abstract pending license holder approval.) Case Study: EM and Seismic Analysis on Prospect in Deepwater Niger Delta Jochen Moser1, Abayomi Adejonwo2, Manuel Poupon1, Hans-Jurg Meyer1, Chris Wojcik1, Mark Rosenquist1 1Shell International Exploration & Production, Inc., Houston, U.S.A. 2Shell Nigeria Exploration & Production Company, Lagos, Nigeria Results of a prospect evaluation in the deepwater Nigeria thrust belt for which integrated

seismic/CSEM technologies played a critical role in reducing residual gas risk are presented

During the last couple of years, drilling activities in the deepwater thrust belt play of Nigeria

have shown mixed results; with some wells finding pay but others coming across low saturation gas and/or brine sands. In this frontier exploration a key risk of the toe-thrust prospects trap integrity, related to both earlier thrust faults and later extensional faults.

Leaky traps commonly result in residual hydrocarbon saturations, which are difficult to

discriminate from commercial saturations with conventional seismic amplitude analysis. Residual gas sands can be particularly deceptive as their amplitude response is often similar to commercial oil saturation. In general, seismic technologies cannot effectively de-risk prospects with residual-gas risk and integration with other technologies is necessary to avoid failure.

The controlled-source electromagnetic data (CSEM), which directly senses resistivity in the

subsurface, has a great potential to reduce the risk of residual saturation. Reservoirs with residual hydrocarbons are characterized by generally low resistivity, while reservoirs with commercial saturations have a much higher resistivity, which can be discriminated by CSEM for prospects in a favorable depth range.

We present the results of a prospect evaluation in the deepwater Nigeria thrust belt for which

CSEM data played a critical role. This example demonstrates the power of integrated seismic/CSEM technologies in reducing residual gas risk. In this particular project, the CSEM survey indicated high risk of residual gas in shallow targets, which was consistent with the well results.

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