Seeing through the CO2 plume: Joint inversion-segmentation of the Sleipner 4D seismic data set

Seismic inversion in geophysics is a method that uses certain prior information, such as known geological laws and well logging and drilling data, to infer the physical parameters of underground media, such as wave impedance, velocity, and density, from seismic observation data, and thereby obtain the spatial structure and physical properties of underground strata. Seismic inversion is a highly complex problem with multiple solutions, and with the advancement of collection equipment, the volume of geophysical observation data is increasing at an astonishing rate. This presents new challenges for the accuracy and speed of seismic data inversion methods. There is an urgent need to develop intelligent and efficient inversion technologies for seismic inversion.Deep learning networks have powerful nonlinear fitting capabilities and can be used to solve complex nonlinear problems, such as seismic inversion. However, the predictive ability of deep learning networks largely depends on the quantity of training data. In the early stages of oil and gas exploration and development, the amount of well logging label data available for training is very limited, which poses a challenge for the application of deep learning in seismic inversion. Semi-supervised learning seismic inversion methods consider both data mismatch issues and well logging data mismatch issues, and can better adapt to inversion problems in real-world scenarios. Unlike supervised learning approaches, semi-supervised learning does not require a large amount of labeled data, thus it can better handle situations of data scarcity or mismatch.This paper utilizes a semi-supervised learning workflow to perform inversion on post-stack seismic data and has conducted experimental validation on the Marmousi 2 model. The experimental results show that, compared to supervised learning networks, the semi-supervised learning network still exhibits good predictive performance with a limited amount of data, demonstrating better stability in the presence of noise and geological variations, and effectively learns the mapping relationship between seismic data and artificial intelligence. Furthermore, as the amount of training data increases, the performance of the network also improves, confirming the importance of data quantity for training deep learning networks. The application results of the network on actual data indicate that the network has broad application prospects and feasibility. However, since the network is based on a channel-by-channel inversion method, there is still a lack of representation in terms of lateral continuity, which requires further exploration and improvement in subsequent research.

Seismic data incorporation in reservoir simulation models history matching (HM) studies has been continuously growing. 4D seismic data, in contrast with well production data, can provide a very good scenario of fluids arrangement along reservoir. In this work we describe how 3D and 4D seismic data gathered in acquisitions performed in Campos Basin was incorporated in Marlim Sul deep water field geological model reconstruction and in assisted HM (AHM). It is taken advantage of both 3D and 4D seismic data in several stages of the study, for instance, in the construction of a new porosity – most influential in impedance – model by using a methodology based on the inversion of synthetic seismic (calculated by petro-elastic model) in porosity through an optimization process that aims to reduce the difference between observed and synthetic impedance, and when defining influential parameters based on fluids displacement registered by seismic signal, by using a technique based on the creation of transmissibility multipliers parameters regions that considers the fluids displacement shown in 4D signal. Another relevant point is the use of information from reservoir and 3D seismic data when weighting the 4D data in the objective function. Combining the above mentioned techniques with the knowledge of the field – supported by the 3D seismic data – which allowed, for instance, identification of faults – where fault transmissibility multipliers were used as parameters in the HM process – a fairly good agreement on the observed well and seismic production data was achieved. HM studies using AHM tools have been shown a much more time-efficient technique when compared to manual HM. The incorporation of 4D seismic data can considerably improve the HM quality by improving the reservoir description, once it increases the ability of describing fluids arrangement and pressure distribution. The techniques successfully applied in the Marlim Sul field HM support these conclusions.

Time-lapse (4D) seismic data sets have proven to be extremely useful for reservoir monitoring. Seismic-derived impedance estimates are commonly used as a 4D attribute to constrain updates to reservoir fluid flow models. However, 4D seismic estimates of P-wave impedance can contain significant errors associated with the effects of seismic noise and the inherent instability of inverse methods. These errors may compromise the geological accuracy of the reservoir model leading to incorrect reservoir model property updates and reservoir fluid-flow predictions. To evaluate such errors and uncertainties we present a time-lapse study based on a 3D reservoir model example, thereby exploring a number of inverse theory concepts associated with the instability and error of coloured inversion operators and their dependence on seismic noise levels. In this example, we use an oilfield benchmark case based on the Namorado Field in Campos Basin, Brazil. We introduce a histogram similarity measure to quantify the impact of seismic noise on maps of 4D seismic amplitude and impedance changes as a function of S/N levels, which indicate that amplitudes are less sensitive to 4D seismic noise than inverted impedances. The root-mean-square errors in the estimates of water saturation changes derived from 4D seismic amplitudes are also smaller than for 4D seismic impedances, over a wide range of typical seismic noise levels. These results quantitatively demonstrate that seismic amplitudes can be more accurate and robust than inverted seismic impedances for quantifying water saturation changes from 4D seismic data, and emphasize that seismic amplitudes may be more reliable to update fluid-flow model properties in the presence of realistic 4D seismic noise.

To improve the accuracy of carbonate lithofacies modeling, mainly well data such as core, thin section and well logging data had been adopted in conventional methods. Although the reservoir types classification is very detailed, but it is usually difficult to integrate with seismic data to make 3D lithofacies model. To address the issue of carbonate lithofacies modeling, a new integrated carbonate lithofacies modeling technique was summarized based on thin section, core, well logging, 3D seismic data and production performance data. The integrated carbonate lithofacies modeling workflow mainly contains 5 steps. 1) Integrated lithofacies classification based on the core, thin section, well logging, FMI, CMR and production performance data. 2) Petrophysics lithofacies classification based on the cross-plot analysis between sensitive well log curves. 3) Petrophysics lithofacies prediction based on the sensitive well log curve by deep learning method, and verification by core lithofacies analysis. 4) Seismic inversion volume optimization by well lithofacies calibration. 5) Lithofacies modelling based seismic inversion based on the seismic inversion cut-off analysis (Fig.2). This workflow integrated seismic impedance (continuous variable) with lithofacies (discrete variable), and converts seismic inversion into lithofacies directly. According to the certification of new wells, this technique had been applied successfully in carbonate reservoir of M oil field in Middle East, it not only improves the accuracy of 1D lithofacies prediction for wells by deep learning method, but also improves the accuracy of 3D lithofacies modeling for the whole oilfield by well and seismic inversion integrated. The lithofacies modeling not only matched with lithofacies from core analysis and petrophysics lithofacies prediction from well log analysis, but also matched with seismic inversion data in no well area. The integrated carbonate lithofacies modeling workflow integrated thin section, core, well logging, 3D seismic data and production performance data, and improved the improves the accuracy of 3D lithofacies modeling for no well area. It’s useful for new wells optimization and high efficiency development with lower cost. The integrated carbonate lithofacies modeling workflow not only suit for carbonate reservoir, but also suit for clastic reservoir.

Integration of 3D and time-lapse 4D seismic data into reservoir modelling and history matching processes poses a significant challenge due to the frequent mismatch between the initial reservoir model, the true reservoir geology, and the pre-production (baseline) seismic data. A fundamental step of a reservoir characterisation and performance study is the preconditioning of the initial reservoir model to equally honour both the geological knowledge and seismic data. In this paper we analyse the issues that have a significant impact on the (mis)match of the initial reservoir model with well logs and inverted 3D seismic data. These issues include the constraining methods for reservoir lithofacies modelling, the sensitivity of the results to the presence of realistic resolution and noise in the seismic data, the geostatistical modelling parameters, and the uncertainties associated with quantitative incorporation of inverted seismic data in reservoir lithofacies modelling. We demonstrate that in a geostatistical lithofacies simulation process, seismic constraining methods based on seismic litho-probability curves and seismic litho-probability cubes yield the best match to the reference model, even when realistic resolution and noise is included in the dataset. In addition, our analyses show that quantitative incorporation of inverted 3D seismic data in static reservoir modelling carries a range of uncertainties and should be cautiously applied in order to minimise the risk of misinterpretation. These uncertainties are due to the limited vertical resolution of the seismic data compared to the scale of the geological heterogeneities, the fundamental instability of the inverse problem, and the non-unique elastic properties of different lithofacies types.

3D seismic data de-noising and reconstruction using Multichannel Time Slice Singular Spectrum Analysis

Seismic data offer valuable spatial information that contributes to the development strategy, such as drilling decisions and well planning. Inverting 3D seismic reflectivity data to impedance unlocks the potential of using seismic data to estimate the lateral variability and to predict reservoir geomechanical behaviour. The geomechanical model contributes for the evaluation of wellbore stability, frackability and drilling direction. The objective of this work is to illustrate the use of seismic derived models for reservoir and geomechanical properties, such as total porosity, total organic carbon (TOC) and rock rigidity. These properties are instrumental to characterize tight reservoirs and useful tools for defining a horizontal well trajectory.The study area has an exploration potential of unconventional reservoirs, targeting tight carbonates from Upper Jurassic. To date, only one well has been drilled in the area of the interest and high quality 3D seismic data is available. The process consists of conducting seismic inversion, using the well and the 3D PSTM seismic data, and resulting in the generation of 3D subsurface impedance model. The well-log relationships between impedance and reservoir quality (effective porosity, source richness) and completion quality (brittleness) were determined and showed high correlation. Hence, 3D rock quality and geomechanical volumes were derived to characterize the behaviour of the reservoir target.Seismic derived properties models yield a better description of the reservoir heterogeneity than using seismic reflectivity data only. The horizontal well trajectory was planned taking into account the observed and significant lateral variability revealed by the 3D models estimated based on seismic inverted data.The results obtained using this methodology demonstrate the importance of integrating geophysical characterisation as a relevant supporting factor in the drilling decision making process. In particular, it validates the use of seismic derived reservoir and completion quality models as an additional tool in the definition of a horizontal well trajectory in a tight/unconventional deep gas target.

In some oilfield where 3D seismic survey has been done, the deeper structure cannot be discovered for poor deep seismic data. Layer stripping technique applied both seismic and gravity data is a solution, but it cannot get satisfied processing effect because the horizontal variation of formation density is ignored. To improve the gravity survey effect in 3D seismic survey area, this paper presents the formation separation technique. Based on 3D seismic depth data and the transformed density data from 3D seismic velocity data, the upper formation's gravity effects are calculated and removed from Bouguer gravity and then the formation‐separated gravity‐anomaly was obtained, which mainly reflects the deeper geological structure. In block XX, the seismic data of shallow formations are excellent but that of top of basement are poor. The formation‐separated gravity‐anomaly processed under the control of 3D seismic data fits well with the known seismic interpretation and wells. It makes the geological interpretation more reliable

Limitations of the seismic method in mapping salt diapirs increase exploration risks while drilling in near-salt areas. An innovative method of 3D joint inverse problem solution for gravity, well-log, and seismic data makes it possible to verify and refine the shape of a salt dome and map hydrocarbon accumulations right beside the salt wall under the salt wing and over the salt dome. It is illustrated by the results for four salt domes in the central axial part of the Dniper-Donets Basin. In half of the cases, joint gravity inversion proved that seismic data provided a reliable salt shape that may be used as the biggest outline. In the other half of the cases, the salt stem outlined by 3D gravity inversion significantly differed from that by 2D and 3D seismic data interpretation. In one case, gravity inversion showed that the size of the salt stem was three times smaller. For all the cases under the salt wing in proximity to the salt dome, near the salt wall, and over the salt dome, localized low-density rock areas were mapped. These areas were associated with commercial hydrocarbon pools. For one, the salt dome location of the commercial pools was verified by drilling. As a result, a new oil field was discovered and named after Academician Schpak. Also, over the same salt dome, the size of the Runovshina field shallow gas pool outline was six times expanded. Four wells drilling outcomes ― three commercial and one dry ― fully confirm the correctness of the shape of the mapped commercial pool with a 100 % commercial probability of success.

Interpretation of three-dimensional (3D) seismic data from the northern part of central Saudi Arabia allowed mapping of the Permo-Carboniferous Unayzah Formation's aeolian sand deposits and fluvial channels. These types of depositional environments have been associated frequently with better reservoir-quality sandstones within Saudi Arabia. The depositional setting of the Unayzah reservoirs can be highly variable and range from glacial, to lacustrine, to meandering fluvial, to aeolian, to braided stream. Seismic amplitude data were instrumental in mapping a truncation of the upper Unayzah reservoir against the overlying Pre-Khuff Unconformity. The use of seismic attributes, such as coherence and curvature, indicated the existence of faulting and a fluvial channel network at the top of the Unayzah Formation. The integration of well and seismic amplitude data, via acoustic impedance inversion, revealed the direction of potentially higher porosity fairways within the channel system. The structural and depositional architecture depicted in this study is being appraised through drilling of an exploration well.

Relevance of the work. The paper considers challenging problems related with outlining of intervals with oil and gas presence in the mature Khylly field by use of latest 3D seismic survey techniques in order to gain larger crude resources base. The purpose of this research is to discover the most promising intervals of target horizons with relatively high reservoir properties outlined by 3D seismic data. The subjects of research are 3D seismic survey data, downhole seismic survey – Vertical Seismic Profiling (VSP) and well logging diagrams. The object of research is the Khylly deposit. The paper describes in brief geological and geophysical characteristics, stratigraphic and lithological features of rocks making the section. It is noted that despite repeated surveys by use of various geological and geophysical techniques, the field setting is not thoroughly studied and it has been covered by 3D seismic survey in 2012. Research results. 3D seismic survey applied across Khylly area is resulted in drawing of 4 structural maps for III and I horizons of Productive Series (PS), Akchagyl and Lower Absheron suites. Taking into account the relevance of structural planes of various stratigraphic levels and III horizon of PS being one of the major reference horizons the paper gives description of structural map drawn for this horizon. The detailed velocity model is designed based on VSP data with wide use of velocity analysis data. It has been made clear that Khylly area has block structure and each block has been described in detail. Based on acquired data it has been recommended to drill exploratory well R-1. Conclusion. Processing and interpretation of seismic data are aimed at solving some geological problems; the main task was to obtain results that ensure the study of the geological structure in the seismic survey area, including tracing of seismic horizons, faults and outlining the areas and section intervals which may be of interest due to possible oil and gas presence. VSP data acquired in well 2012 and velocity analysis made it possible to design velocity model of the section under the study, with the use of which the temporary 3D cube was transformed into a depth cube. The quality of seismic data is good and made it possible to solve the tasks set for this research.

The W block shale in the study area is located at the West Canada basin and belongs to the low-porosity and ultra-low permeability gas-condensate reservoirs. The sandwiched layers in the shale are extremely important for reservoir reconstruction. As the single-layer thickness of the carbonate interbeds in the W block shale is very thin and have multi-stage superposition characteristics, the use of conventional geostatistical inversion cannot effectively and accurately predict the thin sweet spots and interbeds in the shale. This article uses three-dimensional probability constraints as pre-stack geostatistical inversion to replace traditional one-dimensional and two-dimensional face constraint inversion and achieved quantitative prediction of interbeds using well logging and 3D seismic data, so that prediction outcomes fully integrates well logging, seismic data and established a set of 3D probability constraint pre-stack geostatistical inversion process for thin interbeds and sweet spots in the shale. The process mainly includes the following crucial steps: (1) Well logging evaluation of shale reservoir parameters, analysis of shale rockphysical parameters and analysis of petrophysical characteristics of shale sweet spots and carbonate interbeds from well data, in order to evaluate and use seismic data to predict the possibility of shale sweet spots and interbeds. The characteristics of shale sweet spots is low Poisson ratio, moderate Young's modulus, high total organic content (TOC) and high brittleness. The characteristics of carbonate interbeds is high Poisson ratio, high Young's modulus, low TOC and high density. (2) Pre-stack gather optimization to establish six reliable partial sub-offset seismic data volumes. (3) Application of waveform difference simulation for prediction of shale interbeds, analysis of the vertical and horizontal variation characteristics of the interbeds based on post-stack seismic data and the three-dimensional probability volume of carbonate interbeds was estabilished according to the results of seismic waveform difference simulation and well logs. (4) High resolution 3D lithologic probability constrained pre-stack geostatistical inversion are performed and effectively predicted the spatial variation characteristics of shale thin interbeds and sweet spots.

Field repeatability of OBC 3D seismic data is investigated for evaluating feasibility of time-lapse (4D) seismic survey for monitoring water saturation in a carbonate reservoir in the Middle East. We use co-located data sets, which were acquired in a small part of a giant 3D survey by re-deployed shots/receivers at a short time interval, between which no reservoir change is supposed. Both pre- and post-stack seismic data are examined using two repeatability indices: normalized root mean square (NRMS) and predictability, each complementarily quantifying the similarity of two seismic traces, i.e., data repeatability. For pre-stack data, source-receiver mis-location along the offset orientation is found to control the repeatability. Post-stack repeatability NRMS evaluated after basic data processing including noise removal, stacking, and time shift, turns out to be around 10 %. The presence of noise and static shift severely degrades repeatability in post-stack. This data-driven repeatability evaluation takes account of the influences from shot/receiver mis-positioning, source-generated coherent and incoherent noise, and location-dependent near surface effect, which conventional approaches may under-estimate. The post-stack repeatability, however, is evaluated in a small area and can be laterally variable, since the 3D seismic data quality is strongly area-dependent. We therefore correlate the NRMS with "snapshot" 3D S/N (SN ratio of a 3D) of a single set of the repeated 3D and then use this relation for extrapolating the NRMS to the entire field through 3D S/N of the full field 3D. Combining this analysis with the result of our rock physics modeling, which reveals the magnitude of expected change in reservoir response, we conclude the feasibility of 4D seismic technique in this field is area-dependent. The proposed procedure provides more realistic area-dependent 4D repeatability than conventional approaches.

Three-dimensional (3D) seismic data visualization is a critical technique in 3D seismic data interpretation. In the paper, the characteristics of seismic data, the technology of 3D data sets visualization and the technology of 3D graphics construction and display using interactive data language (IDL) were discussed. The author proposed new solutions for 3D seismic data visualization, moreover expounded how to reorganize 3D seismic data and how to realize direct volume rendering and surface rendering using IDL. Using the interface provided by IDL based on COM standard, the author applied the new solutions to practice and built a 3D Seismic Data Visualization Platform..

A case study of how fluid substitution affects the seismic waveforms is presented. Direct hydrocarbon prediction on the seismic data is prone to inaccuracy without investigating the impact of fluids on the seismic waveforms since the seismic data is influenced by mineral composition, porosity, salinity, pressure, and other factors in addition to fluid type. The study relies on 3D seismic data and log data gathered from 3 wells. Petrophysical and rock physics models were computed in order to understand the seismic dataset. In the analysis, prolific hydrocarbon intervals were identified. Then, model compressional velocities and densities obtained for various fluid scenarios were generated by using the Gassmann fluid substitution theory. Normalized bulk modulus approach was used to reduce the ambiguities that shales portend on the velocity models. Model synthetics were generated. The synthetic seismograms were compared with the seismic dataset in order to understand how fluid saturation and lithology impacts the seismic data. In the analysis, two major hydrocarbon bearing reservoirs were delineated. Other mapped reservoirs have characteristic indices that make them unsuitable for the fluid modelling. The results show that the model oil velocity model better approximates the field velocity. The density models produced the same kind of results, suggesting that the reservoir fluid is oil. The generated synthetic diagrams show that replacing the in-situ fluid with either gas or oil models has no appreciable impact on the seismic dataset’s amplitude waveform. This is because seismic reflection amplitude only slightly increases each time gas is assumed. By using the seismic data from this field of study, hydrocarbons cannot be directly predicted from amplitude anomalies because the lithology better influences the seismic waveforms than the fluid composition.