Optimizing geometry based on forward modeling

Time-lapse (4D) analysis of legacy seismic data presents unique challenges, as neither the acquisition nor processing is designed for seismic monitoring. Two legacy seismic data sets from the Lena Field, Gulf of Mexico, are analysed for time-lapse effects. The analysis involves post-stack processing of the legacy seismic data including cross equalization and residual migration, and the definition of a new suite of 4D seismic attributes. These new attributes are used in both processing and interpretation. The time-lapse differences are interpreted using forward modeling and production data. The 4D difference anomaly is interpreted to be the result of gas cap expansion. The identification of potentially bypassed oil based on this interpretation may affect future drilling decisions. Introduction Seismic monitoring (time-lapse or 4D seismic) has the potential to significantly increase recovery in existing and new fields. However, there are many issues associated with the application of time-lapse seismic data. Two of the most significant are repeatability of the seismic data in the non-reservoir portion of the data volume and the robustness and credibility of the seismic difference anomaly within the reservoir (Ross et al. 1996, 1997). While future field developments should benefit from seismic acquisition designed for time-lapse monitoring, the portfolio of current seismic monitoring opportunities for most companies consists of existing fields for which one or more 3D seismic surveys have already been acquired. These legacy seismic data sets were not acquired for the purposes of seismic monitoring and are often very different in terms of acquisition and processing parameters. In addition, the seismic acquisition is rarely timed to optimally map reservoir changes or impact development decisions. Seismic repeatability is sufficient for time-lapse interpretation if the seismic differences in the region of interest are substantially greater than the differences outside the region of interest. The smaller the change in the seismic response due to production, the greater the repeatability required of the seismic data. Seismic modeling incorporating rock physics and reservoir simulation can help estimate the magnitude of reservoir changes but repeatability and interpretability can only be determined by the analysis of multiple seismic surveys. The main goal of this study is to understand the magnitude ofthe processing effort required to obtain reliable time-lapse differences. The reliability of the seismic difference is measured by repeatability in the seismic volume and the reconciliation of the time-lapse anomaly with geologic and production data. Geologic Setting The Lena Field (Mississippi Canyon Block 281) is located south of the modern Mississippi delta in 1,000 feet of water. The field is situated on the western flank of a salt dome within a fault-bounded intraslope basin. Hydrocarbon production is from six Pliocene-age sands. The B80 reservoir is located about 10,000 feet below SL at about 3 seconds seismic TWT. The interval is interpreted as a low-stand fan systems tract representing deposition in distributary lobes composed of amalgamated and channelized turbidities. The updip limit of the sands lies about 2,000 feet west of the salt flank and the reservoir thickens basin-ward to the west.

High Resolution, Simultaneous VSP and Land Seismic Acquisition

Seismic data acquisition is essential for discovering new hydrocarbon targets, where a high-resolution regular-spaced acquisition is critical to obtain high-quality seismic images. However, the high acquisition costs and environmental impacts have motivated designing seismic surveys with fewer sources and receivers than regular-spaced sensing approaches. After the undersampled measurements are acquired, an algorithm reconstructs the missing information necessary for the subsequent processing and interpretation analysis. The removed data is currently selected using random, jittered, and uniform sensing schemes leading to suboptimal seismic image recovery. Therefore, a guided design of undersampled seismic surveys is important as it determines the quality of the reconstructed information. This work proposes an end-to-end (E2E) optimization to design an undersampled seismic acquisition pattern that preserves the high quality of the reconstructed data. The sensing pattern is modeled as a deep binary layer to learn the location of receivers and sources for a particular seismic survey. Simultaneously, a deep neural network recovers the underlying removed data. Once the sensing pattern is designed, it can be used as a seismic acquisition geometry in an area that exhibits a similar geological setting to the training dataset of the E2E model. Extensive experiments were conducted on synthetic and real seismic data from different geological settings. The proposed design was compared with the traditional random, jittered, and uniform sensing schemes. The results validate that a guided design improves the quality of the reconstructed data by up to 4 and 2 dB in peak-signal-to-noise ratio for trace and shot gather reconstruction, respectively.

Seismic reflection data are used by geologists to identify the best site for oil and gas explorations. The raw seismic image cannot be directly used for explorations because the noises may hinder the primary reflections and result in misinterpretations. In the proposed research work, the denoising method with adaptive dictionary learning is attempted to attenuate the coherent and random noises which affect the seismic reflections. In this method, Karhunen Loeve Transform (KLT) is combined with K-means Singular Value Decomposition (KSVD) to improve the Signal to Noise Ratio (SNR) of the seismic data. The combination of fixed transform using KLT and learning-based dictionary using KSVD remove the redundant data while retaining the necessary data for further interpretations. KLT is applied on the whole seismic image to decorrelate the coefficients and retain the primary reflections. The horizontal events in the seismic image are preserved as they represent the eigen images with large energy. The KSVD is applied to the KLT resultant data, which denoises the patch of data while simultaneously updating the dictionary. The noise is eliminated using local sparsity of the image where mutually overlapping small image blocks are learned to yield the self-adaptive redundant dictionary. It is then used to obtain the sparse representation of the image blocks by eliminating noise. The algorithm is tested for both synthetic and field seismic images, and the results indicate a better reduction of random and coherent noise with the acceptable execution time compared to other denoising algorithms used. The KLT combined with KSVD method decorrelate the seismic data from noise by preserving the primary reflections and discarding the redundant noise.

Thin (<20 m), sand injectites, too small to resolve in conventional seismic reflection data, can constitute a large part of the net-to-gross volume and affect fluid flow in the reservoir. However, they may also cause challenges for well placement and reservoir development because they are too small to be reliably imaged in seismic reflection data. It is therefore important to better understand how small-scale injectites influence seismic images, and how they may be recognized and characterized above reservoirs. The Grane Field (North Sea) hosts numerous small-scale sand injectites above the main reservoir unit, challengin well placement, volume estimates and seismic interpretation. Here, we investigate how such small-scale sand injectites influence seismic images and may be characterized by (1) using well, 3D seismic and outcrop data to investigate geometries of small-scale sand injectites (0–15 m thick), (2) performing seismic convolution modelling to investigate how these would be imaged in seismic data, and (3) comparing these synthetic seismic images with actual 3D seismic data from the well-investigated Grane Field. Our results show that despite injectites being below seismic resolution, small-scale sand injectites can be detected in seismic data. They are more likely to be detected if they have high thickness (>5 m), steep dip (>30°), and when densely spaced. Furthermore, as the fraction of sand injectites increases the top reservoir amplitude will decrease. Moreover, comparison of the synthetic seismic images with real seismic data from the Grane Field indicates that the low-amplitude anomalies and irregularities observed above the reservoir may be a result of the overlying sand injectites. Additionally, the comparison strongly suggests that the Grane Field hosts sand injectites that are thicker and located further away from the top reservoir than is indicated by well observations. These results may be used to improve well planning and develop reservoirs with overlying sand injectites. Supplementary material: A PDF file containing all the seismic modelling results allowing the reader to flip back and forth between the models is available at https://www.doi.org/10.6084/m9.figshare.14333102 and well logs from well 25/11-18T2 are available at https://factpages.npd.no/pbl/wellbore_documents/2358_25_1_18_COMPLETION_REPORT_AND_LOG.pdf

The application of the seismic reflection method to detect stratigraphic changes away from well locations is beginning to gain acceptance among Reservoir Engineers. Unfortunately one of the limitations of this method is the inherent low resolution of seismic data as compared to well log data. Several approaches have been proposed to produce well log-like or pseudo log traces from seismic reflection data. Although there is no improvement in the resolution, the pseudo log traces provide a more convenient way of visually corelating well data to seismic data. A new stratigraphic extrapolation method designed to overcome some of the drawbacks of low resolution is presented. This method consists of relating a model trace (initially derived from well information) to an actual seismic trace by varying the parameters of the model trace within well defined constraints until best match is achieved between them. Extrapolation of the stratigraphy away from the well is performed from trace to trace, the starting model for each trace being determined either from the final model for the previous trace or from an independently postulated model. An interactive computer program is employed for this purpose which allows the incorporation of additional information to the initial model. In this way the final stratigraphic model of the reservoir is the one which best matches the seismic data. Several examples showing the application of the method will be discussed.

Presented on 28 May 2025: Session 22 The Bonaparte Basin is well-positioned to support Australia’s Future Gas Strategy, with proven gas-prone petroleum systems ideal for future carbon capture and storage (CCS) and liquefied natural gas (LNG) development. From a drilling perspective, the shallow water depths (70–230 m) present accessible resource opportunities but pose significant challenges for seismic survey operations and subsurface imaging. In early 2024, following a detailed survey design study, a seismic acquisition was carried out using a single vessel towing 12 streamers of 8000-m length with 112.5-m streamer separation, and three acoustic sources at 50-m shotpoint intervals. This survey aimed to improve subsurface imaging of the eastern Vulcan Sub-basin and Londonderry High. Despite challenges – restricted manoeuvrability, carbonate pinnacles, strict environmental controls, plus local and foreign fishers – seismic data were acquired using continuous-line acquisition (CLA) in a racetrack pattern. This approach significantly reduced the survey duration, completing 6808 km2 in just 76 days. Acquiring seismic data with a wide streamer tow and CLA in shallow water demands additional signal processing considerations. The sparse near-offset coverage poses challenges for demultiple, velocity model building, and imaging. To address this, innovative seismic imaging technologies and workflows were deployed. The resulting new seismic image provides improved understanding of the subsurface previously restricted by the quality of legacy seismic which was unable to resolve shallow carbonate intervals and complex faulting. Along with the results, we will also discuss how these technologies could shorten future seismic survey duration and enhance reprocessing of legacy seismic data. To access the Oral Presentation click the link on the right. To read the full paper click here

Seismic data interpolation and denoising play vital roles in obtaining complete and clean data in seismic data processing. Seismic data usually misses along various spatial axes and always mix with random noise. In order to obtain complete and clean seismic data, reconstruction technology can interpolate missing data and attenuate random noise. Nonsubsampled contourlet transform is an effective transform to obtain multi-scale and multi-direction sparse domain data for compression sensing interpolation and denoising. However, conventional iterative shrinkage/thresholding cannot handle ill-posed and ill-conditioned equations for solving linear inverse problem. We present a two-step iterative log thresholding method to overcome ill-posed and ill-conditioned problems and improve the convergence rate and solution accuracy, which can interpolate and denoise seismic data simultaneously in the nonsubsampled contourlet transform framework. First, we use nonsubsampled contourlet transform to convert the seismic missing data with random noise to sparse domain. Then, we apply two-step iterative log thresholding algorithm to interpolate and denoise data in sparse domain. The result of each iteration is based on the results of the previous two iterations, which can accelerate convergence rate. In addition, log thresholding can further improve convergence rate and solution accuracy. Finally, we use inverse nonsubsampled contourlet transform to obtain the interpolated and denoised seismic data. The new method can reconstruct the irregularly missing data and attenuate random noise to obtain complete and clean seismic data with high accuracy, which is crucial for seismic imaging and inversion. We demonstrate the applicability and effectiveness of this simultaneous interpolation and denoising technique with successful applications to both synthetic and field data examples.

As a kind of supergene geological phenomenon, ground fissure has brought great inconvenience to human life. In addition, it also has a close relationship with earthquake. However, it is very difficult to ascertain the extension depth of ground fissure since its concealment and uncertainty. In this paper, 3D seismic exploration is used to detect ground fissure in Shanxi Province of China. Specific parameters for seismic data acquisition, processing and interpretation are analysed. Firstly, seismic data acquisition method and its corresponding parameters are discussed. Small dose explosive sources and high frequency geophones are used. Small trace interval and appropriate fold are also adopted. Secondly, seismic data processing is processed from shot record to seismic profile. Multi-domain loop iteration de-noising is used to get high signal-to-noise ratio data. Accurate near surface model, interactive iteration and residual static correction are used to eliminate the impact of low velocity zone and the static correction problem. Large common middle point bin and small velocity analysis interval are used for high accuracy velocity spectrum analysis. The mute parameter of stretching distortion and the migration aperture are researched for shallow ground fissure detection. Thirdly, seismic data interpretation is processed to get ground fissure distribution. Fault enhanced filter is used to improve the signal-to-noise ratio effectively and the chimney cube is used to identify ground fissure automatically. Thus, the specific 3D seismic exploration method used in this paper is suitable for ground fissure detection. Cited as : Shi, S., Liu, Z., Feng, J., Feng, G., Li, M. Using 3D seismic exploration to detect ground fissure. Advances in Geo-Energy Research, 2020, 4(1): 13-19, doi: 10.26804/ager.2020.01.02

Björn Paulsson, Martin Karrenbach, Paul Milligan, Alex Goertz, and Alan Hardin of Paulsson Geophysical Services, with John O'Brien and Don McGuire of Anadarko Petroleum Corporation explain why recording multi-component seismic data using receivers positioned deep in the earth, and closer to the target-zone, can overcome many of the limitations experienced by surface 3D seismic methods. Borehole seismic surveys, commonly known as Vertical Seismic Profiling (VSP), have been an industry standard technique for several decades. In the past, however, these data have been used primarily for check-shot type velocity surveys and for reflection mapping at the well location in a one-dimensional fashion. This 1D measurement can be extended to 2D by using one or more walk-away lines of surface source points. The 2D method works well enough for imaging simple layered stratigraphy, but in a complex reservoir a full 3D data acquisition and imaging solution needs to be pursued. Inserting seismic sensors deep into oil and gas wells, as shown in Figure 1, allows the recording of much higher frequencies as compared to placing sensors at the Earth’s surface. The reason for this is simple: seismic waves have to propagate only once through the weathered layer in a confined zone near the source. In contrast, during surface seismic surveys, waves must travel through the weathered layer twice. Each traversal of the weathered layer attenuates high frequencies much more than the low frequencies, thus reducing the image resolution. The frequency content of borehole seismic data is typically more than twice that of surface seismic data, which provides an increase in subsurface resolution. In addition to recording higher frequency data, borehole seismic sensors provide a number of other advantages: borehole seismic data typically achieve a much higher signal-tonoise ratio than surface seismic data. The combination of a quiet borehole environment and strong sensor coupling to the borehole wall enables such high signal-to-noise ratio. Surface geophones, on the other hand, are generally poorly coupled in weathered rock and exposed to cultural and environmental noise at the surface. Good sensor coupling in the borehole enables three-component (3C) seismic data to be recorded with high vector fidelity. This ultimately allows shear and converted-wave imaging as well as the determination of anisotropy by shear wave splitting analysis (see, e.g., Maultzsch, 2003). Combining P and S wave images allows for attribute inversions of rock properties, such as fluid content, pore pressure, stress direction and fracture patterns. O’Brien et al. (2004b) use time lapse borehole seismic to map changes in such critical attributes for production monitoring purposes. Another advantage of borehole seismic surveys is a favourable geometry to illuminate complex structures such as sub-salt targets, salt flanks or steeply dipping faults. The 3D image volume that can be generated from a large downhole seismic array data is shown in Figure 1. The typical 3D borehole seismic image volume is cone shaped with the top of the cone coincident with the top receiver in the borehole array. The size of the base of the cone is determined by the depth of the image volume and the offset of the sources.

Seismic reflection amplitude data are increasingly used in reservoir modeling to provide information on changes in earth properties away from well locations. In geostatistical reservoir modeling, the most common application is to use seismic data as background data in some form of comodeling. Seismic data image reflectors and not earth layer properties. Therefore, prior to use in comodeling, seismic data must first be transformed into an earth layer property. Typically, the transform is to acoustic impedance using an appropriate seismic inversion method. Seismic inversion methods generate results that are generally band limited in nature, resulting in limits to vertical resolution. The vertical resolution achieved can be an order of magnitude below the vertical model resolution required from geostatistical reservoir modeling, which is in the order of well-log resolution. Hence, in using seismic data, geostatistical modelers encounter a problem of downscaling, not the more commonly encountered upscaling problem. This difference in scale introduces scatter between the primary data with well-log order resolution and the secondary seismically derived rock property data used in the comodeling. As a result, to preserve vertical heterogeneity, only limited use of the secondary data can be made in comodeling procedures. This results in models that only partially fit the seismic data, i.e., only limited use is made of the seismic information. If the secondary data are more strongly imposed, the fit to the seismic data improves, but the required vertical heterogeneity is not preserved. The inability to overcome this difference in scale issue, therefore, limits the value of the application of comodeling methods to integrate seismic data into reservoir models. One class of geostatistical methods that overcomes this limitation relies on iterative geostatistical modeling. In these methods, referred to as geostatistical seismic inversion, the iterative modeling process is conditioned such that the final models generated closely match the seismic data while maintaining the required vertical heterogeneity. The application of these methods is computationally expensive relative to comodeling methods but is now practical for large models on today's desktop hardware. Relative to comodeling, geostatistical seismic inversion methods make full use of the information carried in the seismic data, resulting in a significant reduction in model uncertainty away from well control.

Despite proven analogues elsewhere in Malaysia, pre-carbonate hydrocarbon targets offshore Sarawak have met with limited drilling success. This has been mainly attributed to the poor seismic imaging beneath complex velocity carbonate reefs and platforms with low signal-to-noise ratio and inadequate illumination. Seismic imaging is challenging due to inconsistent illumination, multiple energy contamination and complex wave propagation. A collaborative study was undertaken to model these challenges and recommend optimum acquisition and signal processing solutions to produce a step change in seismic imaging of the pre-carbonate targets. The study involved building a detailed anisotropic 3D earth model using borehole and seismic data covering more than 600 km2 area to create a robust synthetic seismic dataset. Geological and petrophysical review preceded seismic interpretation and simultaneous AVO inversion results were used to populate a fine scale model. Testing and validation of the model was performed on a data subset to ensured the kinematics and amplitudes were consistent with the actual seismic response. The model was then used to create over 15 billion prestack traces using an anisotropic finite difference wave propagation modelling algorithm. From this data, different survey designs were extracted including; narrow-azimuth (NAZ), multi-azimuth (MAZ), wide-azimuth (WAZ) and full-azimuth (FAZ) circular shooting streamer geometries. These datasets were processed and analyzed to test optimal demultiple and imaging approaches and to understand the relative benefits of different acquisition geometries. The results indicated that acquiring azimuthally rich data improves illumination of the pre-carbonate section. The demultiple testing successfully demonstrates the workflow needed to attenuate both short and long period multiples. Reverse Time Migration (RTM) produced an improved image compared to Kirchhoff pre stack depth migration. This case study demonstrates that large scale 3D finite difference seismic modelling, aimed at addressing specific geological challenges, is an effective tool in understanding the optimum acquisition design and processing workflow prior to beginning a new seismic acquisition. Based on the findings of this collaborative study, PETRONAS has now acquired a new seismic data set in the area targeting pre-carbonate objectives using a 3D full-azimuth circular acquisition.

Key technologies for complex surface seismic acquisition in the Sichuan Basin and their application effect

Defining the root zone of mud volcanoes (MVs), structural interpretation, and geologic modeling of their body is a problematic task when only seismic data are available. We have developed a strategy for integration of gravity and seismic data for better structural interpretation. Our strategy uses the concept of the normalized full gradient (NFG) for integration of gravity and seismic data to define geometry and the root zone of MVs in the southeast onshore of the Caspian Sea. Our strategy will increase the resolution of the seismic envelope compared with the conventional Hilbert transform. Prior to interpretation, we applied the NFG method on field gravity data. First, we perform a forward-modeling step for accurate NFG parameter definition. Second, we estimate the depth of the target, which is the root zone of the MV here. Interpretation of field gravity data by optimized NFG parameters indicates an accurate depth of the root zone. Subsequently, we apply the NFG method with optimized parameters on a 2D seismic data. Application of our strategy on seismic data will enhance resolution of the seismic image. The depth of the root zone and the geometry of the MV and mud flows was interpreted better on the enhanced image. It also illustrates the complex structure of a giant buried MV, which was not well-interpreted on conventional seismic image. Interpretation of the processed data reveals that the giant MV had lost its connection to its reservoir, whereas the other MV is still connected to the mud reservoir. The giant MV is composed of complex bodies due to pulses in the mud flows. Another MV in the section indicates narrow neck with anticline and listric normal faults on its top. Thus, application of the NFG concept on seismic image could be considered as an alternative to obtain enhanced seismic image for geologic interpretation.

Correct understanding of the influence of key acquisition parameters on seismic imaging is helpful to design a reasonable seismic acquisition geometry so as to acquire high quality seismic data. Although the forward modeling method we conventionally use can help to study and design acquisition geometry to some extent, it is obviously more convincing to study the geometry based on actual acquired seismic data. In X oilfield in Bohai Bay of China, three times of seismic acquisition with quite different geometries have been carried out successively, which provides a data basis for us to deeply analyze the influence of acquisition parameters on seismic imaging. In order to change the acquisition parameters of the actual acquired seismic data, a new geometry degradation method is proposed. Therefore, in this paper, we studied the influence of key acquisition parameters including acquisition direction, bin size and fold on the imaging of typical geological bodies such as gas cloud and sandbody through the method of geometry degradation. Finally, we have obtained some meaningful conclusions that can guide seismic acquisition design. Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2021 in Denver, Colorado.