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In the past decade, Petroleum Development Oman (PDO) has been actively drilling deep tight gas wells targeting structural four way dip closures in the Miqrat Formation (middle Cambrian). The Miqrat Formation was widely deposited in an arid to semi-arid continental setting in alluvial and playa/sabkha environments. The wide distribution of the Miqrat formation and the lack of high resolution seismic together with unavailability of dense well information imposed various challenges in developing accurate subsurface depositional models. The play depositional model was constructed as simplistic as possible assuming a wide-spread playa which tends to be sandy in the south and muddier toward the north. In addition, the previous models were forcing the correlations to fit the entire reservoir subdivisions (Upper, Middle & Lower) to be present within the play domain. This served the regional understanding for prospect identification. However, when zooming into field-scale areas it becomes obvious that the depositional model needs further refining to understand the well data and the flow behaviors in various domains. Recently, with the arrival of high resolution wide azimuth seismic data and the availability of denser well penetrations, a relook was applied to the previous depositional model by integrating various multidisciplinary inputs including: well log evaluations, seismic inversion for reservoir characterization, well test results and pressure measurements. The data integration revealed that the Miqart reservoir properties and thickness are highly influenced by the paleo structural elevation variations and the accommodation space post the Angudan unconformity (poor properties close to the highs). These variations resulted in lateral change to thinner/poorer units or onlaps on dominant highs, and the existence of capillary trapping ( Figure 1 ). As a result, the tighter and thinner facies retained significant amount of water which did not succeed to get flushed by late gas charge due to capillary barriers. Therefore, wells which are drilled in good reservoir properties in a structurally down dip position show better gas saturation with high gas and low water production rates in comparison to the wells which are drilled up dip in poor properties within the same traps ( Figure 2 ).

For any reservoir engineering issue or manage production from the petroleum reservoir, it is required to have seismic characterizations in quantitative manner, rather than qualitative geological interpretations. Herewith, seismic inversion could assist reservoir engineer as the technique to transform seismic data to quantitative rock properties. General steps in interpretation of seismic data preparing for porosity estimations consist of seismic structural interpretation, inversion procedure and attributes analysis. Since there is no direct measurement for the lithological parameters, they are to be computed from other geophysical logs or seismic attributes. This process also requires repeated intervention of the experts for fine tuning the prediction results. Standard regression methods are not suitable for this problem due to the high degree of the unknown nonlinearity. The problem is further complicated because of uncertainties associated with lithological units. In this context, Artificial Neural Network is considered to be useful tools to establish a mapping between lithological and well log properties. In this study, a strategy is presented for defining 3D seismic reservoir porosity model based on advanced method of artificial intelligence (AI) concept. This strategy then would be applied on a complex and heterogeneous oil reservoir which is a relatively symmetrical anticline whose trend is N-S. Required input data was prepared by seismic attribute and the velocity was modeled by vertical seismic profiling data. The general characterization strategy followed by initial inversion model construction for acoustic impedance of total cube for the target formation. Consequently, initial inversion model for effective and total porosity of the target formation was obtained. Acoustic impedance logs were used for neural network training and the genetic algorithm were used for calculation. High correlation values around 86% in cross plots, confirm accuracy of the porosity estimation by the AI method. This model then was used to precise the geological and geometrical properties of the reservoir for well location proposal.

We present a study of seismic inversion using deep learning tools. The purpose is to investigate the feasibility of using neural networks to construct acoustic and elastic earth models directly from pre-stack seismic data. Training and testing of the neural networks are done using thousands of synthetic 1D earth models and seismic gathers. We use 2 different types of neural network architectures in our numerical experiments to investigate seismic inversion in different geological scenarios. In both cases, the quality of the prediction is comparable with that obtained from conventional model building processes as such as travel-time and waveform inversion methods. The predicted earth models contain abundant low and medium wave-number information. In terms of performance, training took only less than 30 minutes on 4 GPUs whilst prediction adds negligible cost.

Summary In oil exploration today, many easy targets are exhausted and seismic imaging is often relied on in areas that suffer from variable illumination due to complex overburden heterogeneity. At the same time, seismic data are increasingly expected to reveal subsurface rock properties and fluid information. To achieve this, rock physics analysis and property estimation using seismic inversion are essential, but these methods assume that changes in seismic amplitudes reflect changes in lithology, which may not be the case for an onshore seismic dataset formed from a merge of different acquisitions and affected by complex overthrust with large velocity contrasts. In this case study we demonstrate how least-squares migration in the image domain was successful in improving seismic images and inversion results for an onshore Mexico project.

Equinor with Shell and Total, evaluate the feasibility for full-scale CO2 capture and storage project in Norwegian continental shelf. One of the challenges for CO2 storage sites is to assure containment and to assess possible leakage paths to the surface. Understanding the overburden’s geological setting is crucial for this assessment. As part of the overburden risk assessment, we investigate the potential for quantifying porosity from seismic inversion data. Knowledge of the porosity distributions, may enable us to employ porosity-permeability models in the future to assess leakage pathways into the overburden.

Summary In order to explore and exploit natural resources in Mexico, it is required high performance technology and engineering human capacity, with both it is creating methodologies to offer a better quality results. The exploration of natural resources, it is the area of knowledge in which geophysics and geology are practiced, however for these two sciences to be understood in the same space, subsurface images are needed. So, implementing an efficient method to avoid noise signals caused by the same time reverse migration algorithm is the method of absorbing borders. This method proposes to subdivide the seismic image into borders in order to obtain only the propagation and retro-propagation signal without spurious signals. This implementation in the oil industry is necessary to generate greater effectiveness in the seismic inversion and thus the generation of seismic images with a better interpretation. Without this implementation, we have worked in the oil industry for more than 30 years, which is why it is innovative for our oil field projects in Mexico to carry it out. It can be said that when referring to the images in depth we are talking about deep migration PSDM (Pre-Stack Depth Migration) and its elements are input data, pre-processing, migration algorithm and speed model. Reverse-time migration (RTM) is a pre-stacking migration technique that, unlike the rest of migrations, ignores simplifications and uses the full-wave equation [Whitmore, 1983] . In the past decades a great variety of absorbent borders have been developed, especially for the modeling of the seismic wave. Subsequently, among the various attempts to improve the classical PML, (Kuzouglu, 1996) and, (Roden, 2000) developed the convolutional PML or CPML (Convolutional Perfectly Matched Layer) for the Maxwell equations and were adapted to the equations of elastodynamics by Komatitsch and Martin, ( Komatitsch, 2007 ). The latter are used to simulate the direct problem in the present work. The development of the reversal algorithm in time, consisted in the implementation of absorbent borders in the different domains of the seismic image. This implementation considerably improves the seismic image compared to the inverse algorithm without absorbing borders and even with absorbent borders not as elaborate as the development in this thesis work. By means of the implementation of convolutional absorbent borders, it modifies the way of thinking of the current algorithms of programming, for which this thesis work is a clear example of the algorithmic revolution applied to the geophysics of the last decade.

This paper provides for a comparison of the classic seismic inversion technique with several multivariate predictive techniques based on machine learning algorithms (linear regression, ACE regression, Random Forest, Neural Network) using seismic data and well logs to estimate rock physical properties. Currently, the estimation of reservoir properties is commonly based on comparative analysis of the distribution of the properties in wells and the elastic properties according to the results of the seismic inversion. Most of the commercial seismic inversion technologies are based on the classical one-dimensional models of seismic exploration involving plane-parallel medium that is described by the linear convolution equation. However, presumably, in some complex cases the linear operators of convolution type cannot properly describe the seismic field distribution. It is assumed that there is a nonlinear absorption of energy of seismic waves in some geological environments, such as in fractured, fluid - rich layers. Such nonlinearity can be shown by vertical and horizontal changes in the seismic wavelet. This paper aims to demonstrate that in certain nonlinear cases using a nonlinear predictive operator based on machine learning algorithms allows to estimate rock physical properties more accurately.

Summary Cyclic Steam Stimulation (CSS) operations for Heavy Oil production in the Lower Fars formation of South Ratqa field located in North Kuwait is being monitored using 3D Vertical Seismic Profiling (VSP) and 4D surface seismic methods. 4D seismic inversion has been integrated with production data by assessing the cycle stage of individual wells, production and injection data, measured temperatures of emulsion, and pressure fall off data during soaking period. To qualitatively cross-check the results, analytical and numerical methods were also invoked to show good correlation between injected steam volume and the size of steam anomaly around the injectors at the time of the acquisition. The orientation of the steam anomalies is consistent with the measured temperatures of production streams. The lesson learnt from the CSS pilot study proved the significance value of 4D seismic in monitoring of the CSS mainly in injection mode and follow-up steam flooding.

As petroleum resources run out, subtle stratigraphic reservoirs have become an important source of future discoveries. Continental tertiary deposits of fluvio-deltaic origin are known to produce such complex reservoirs and pose a challenge for characterization. We propose a novel bio-integrated framework called multi-modal machine learning fusion to tackle this challenge. The approach bases reservoir characterization on biological interpretation of sensory data. Using seismic attributes as varied modal inputs and rock physics relationships as input data, we perform multi-modal machine learning fusion to predict the gamma ray spatial distribution. Before processing however, the seismic data is filtered with a dip steered filtering algorithm to remove random noise. The resulting volume is then used in computing the seismic attributes. Effective seismic attributes utilized were, instantaneous amplitude, instantaneous frequency, spectral decomposition, seismic inversion and similarity. Results from a case study in the Bohai Bay Basin in Eastern China demonstrate that multi-modal machine learning fusion can predict the spatial distribution of lithology using gamma ray logs. The conclusions from this study can be applied in analogous reservoirs in similar petroleum provinces in other regions of the world and can also be applied to other geophysical interpretational methods.

Summary In areas with an active tectonic past, the analysis of the sealing properties of faults is an important aspect both for the understanding of HC migration pathways from the source rock into the trap and for the evaluation of the internal trap structure and integrity. We present a workflow, which utilizes the whole amount of well and seismic information to evaluate the key factors on which the fault seal properties depend. Our approach includes detailed structural interpretation using special seismic attributes, restoration of the tectonic history, mapping of indications of vertical fluid migration from seismic data. We also create a detailed 3D lithological model based on the results of seismic inversion, from which attributes like Juxtaposition and SGR can be extracted. The advanced use of the seismic data makes the workflow also applicable in areas with sparse well control. The presented workflow helps to mitigate the risk at each stage of the E&P process. It results in a more realistic understanding of reserves distribution and hydrocarbon retention in faulted traps, including the potential allocation of additional reserves in undrained blocks. It should also lead to a better planning of the number and location of wells to be drilled.

Summary Salt tectonics contribute to create a suitable environment for oil migration and trapping and, consequently, the saltrich basins are present in many of the most prolific oil and gas provinces in the world. Under high pressures and temperatures, salt can deform and flow during a short period. Hence, the geomechanical characterization of these rocks cannot be neglected during field development studies. Typically, the reservoir pore pressure changes during injection and production. Such changes in the reservoir pressure lead to modifications in the stress state of both the reservoir and its encasing rocks. The building of a geomechanical model requires a multitude of rock mechanical data, such as poisson ratio, Young modulus, density, cohesive strength and so on. Seismic inversions are natural candidates to estimate the elastic property variation of subsurface. We will present a seismic-driven geomechanical characterization of a presalt carbonate reservoir. We propose to assess the elastic property of salt rocks applying rock-physics analysis and seismic inversion. Along with the flow simulation, seismic-derived elastic estimation is coupled to refine the geomechanical simulator.

Summary “A seismic dataset (AF3D) has been inverted for reservoir characterization of an unconventional shale oil play, onshore Argentina. The input are wide azimuth data with long offsets and a high fold stack. An anisotropic PreSDM workflow optimizes resolution and preserve the amplitudes. The following rock physical parameters are estimated for the Mesozoic Vaca Muerta Formation: P-impedance, S-impedance, density, TOC, thickness, maturation, hydrocarbons in place, permeability, porosity, brittleness, pore pressure, Poisson’s Ratio and Young’s modulus. Differentiation between brittle and ductile rocks represents a convenient method for outlining sweet spots. One estimated elastic parameters is Young’s modulus (E), which relates to brittleness. Low velocities correlate with high TOC, higher maturation level, shale content and also overpressures. Small micro cracks and fissures are related to tectonic stress regime or they are maturation driven. The amount of anisotropy in the Vaca Muerta deposits helps to get a better grip on the direction of these micro fractures. Possible sweet spots can be identified based on the mapped reservoir characteristics and estimated rock physical parameters. The seismically derived information allows to optimize the well orientation and spacing as well as and frac design, it has a clear impact on the recovery factor for the unconventional resources.”

Summary Integrated seismic reservoir characterization of carbonate rocks potentially thwarts difficulties arising from reservoir heterogeneity owed to a complex geological history. The featured interdisciplinary workflow reconciles geological, geophysical and engineering components to address reservoir complexity in terms of stratigraphic architecture coupled with a distribution of layer properties that honors flow zonation. Primarily, this workflow calls for a combination of hydraulic flow unit definition embedded in sequence stratigraphy and is further augmented by seismic attribute analysis (i.e., seismic inversion, frequency decomposition of amplitude, etc.), rock physics, and geostatistical techniques to characterize an UAE onshore oil reservoir located within a Lower Cretaceous carbonate sequence (i.e., lower member of Shu´aiba Formation; Strohmenger et al., 2010 ). Using the aforementioned data, we construct an acoustic impedance model and proceed to invert the data in a deterministic and stochastic inversion. Using rock physics, we generate a probability cube of the reservoir properties distribution in a very complex carbonate setting.

Summary Nowadays, many kinds of shear-wave forecasting methods have been provided by researchers, but most of those methods still remain in the stage of good agreement between forecasted curve and measured curve. It is a burning issue and it is difficult to extend it to those wells which lack of shear-wave data, and it has seldom been used in actual pre-stack seismic inversion with a good result. It is critical that the shear-wave velocity estimation to identify the sandstone reservoir using pre-stack simultaneous inversion in old fields which calcium developed and absent shear-wave velocity. In this case, the calcium-bearing shaly sand formation model was established to estimate shear-wave velocity. It was found from the early research that the porosity and mineral content was the key to shear-wave estimation. And the result’s accuracy of logging data interpretation was improved through the well logging formation components analysis method to a great degree. The shear-wave estimated method of Xu & White model and an improved method for calcium-bearing shaly sand formation was applied into reservoir prediction separately. It shows that the sandstone reservoir could be identified clearly from shaly sand formation which contains calcium from the results inverted through the improved shear-wave.

The maturity of source rocks due to their shallow burial is a critical factor in the Central North Sea. In order to examine lateral variations of maturity of the source rocks, quantitative analysis of seismic data is required. The motivation for this study is to investigate the effects and relative impact of organic content, thermal maturity and hydrocarbon generation on elastic properties of organic rich shales. Using some established models, realistic approximations and reference trends that are calibrated to our area and applicable to our well log data, we can begin to predict how our data will behave as conditions change. We observe consistent compaction trends for immature to early mature shales at similar levels of TOC, and can from there infer the effect of hydrocarbon generation in data from a deeply buried, mature source rock. These trends can potentially help us evaluate seismic inversion results and data from other wells where TOC content and source rock maturity are in question.

Summary Seismic inversion is a powerful reservoir characterization tool. Due to the bandlimited nature of seismic data, we need to build a low frequency model for inclusion in seismic inverted result to obtain absolute values of elastic parameters. The most commonly used method to build a low frequency model is through the simple interpolation and extrapolation of well log data within a stratigraphic framework, which leads to a geology unrealistic low frequency model and thus biases the inverted result. To some degree, a geology-featured model can significantly improve the quality of the inversion result. We report a low frequency modelling method called seismic facies-controlled modelling in this abstract. The core idea of this method is to take advantage of seismic waveforms to guide the procedure of low frequency modelling. We apply this method to an impedance inversion of a tight sandstone reservoir. The inversion result clearly delineates geometry of this reservoir and lends a geologic significance to this reservoir distribution.

Summary Prestack scaling is an essential step which is used to correct the amplitudes of seismic reflection data as part of quantitative interpretation workflows such that they honour the background AVO behaviour. The resulting scalers are fed into prestack seismic inversion algorithms to yield better results. Prestack scaling analysis is often performed in the reflectivity domain, however, we propose an algorithm to optimise these scalers further by defining an optimisation engine based on minimising the misfit between measured and inverted acoustic and shear impedances along selected wells. Also, we add constraints to avoid unstable solutions due to possible local minima. In this paper, we show two examples of this algorithm; a synthetic example which shows how the technique should work and a real dataset example where we compare the inversion results before and after amplitude scaling optimisation.

Summary In this paper we present a case study from onshore in the north Western Siberia deepwater sediments, where quantitative interpretation of seismic inversion attributes was based on a rock physics and statistical framework. A lot of companies try to implement Poisson Impedance for encasing contrast between sandstone reservoir and shale for determining situation where difference is small. Poisson Impedance is a difference between the P-impedance and scaled S-impedance (PI=Ip-c*Is). Reservoir and tight sandstones in Achimov formation has approximately the same values of Poisson Impedance compared to the appropriate separation between shale and sandstone. Therefore, deterministic simultaneous inversion along with a Bayesian classification are followed in order to identify distribution of lithofacies using Poisson Impedance and P-impedance.

Summary Rock physics studies help to gain a deeper understanding of the seismic response, and predict effects on the seismic signal caused by hydrocarbon production. Consequently, using rock physics together with 4D seismic is key to understand the time-varying behaviour of a producing field. These techniques are applied to the South Arne field, with the aim to understand how hydrocarbon production is affecting the chalk reservoir. The methods used include rock physics studies, time-shift analysis, amplitude and AVO analysis, and AVO seismic inversion. The main observations from the rock physics studies are that the Young’s modulus is highly dependent on porosity and the acoustic impedance dependent on oil saturation. The time-shift analysis reveals that a limited part of the reservoir is affected by compaction. This part of the reservoir correlates well with an area of high porosities and low Young’s modulus. Seismic hardening is observed for several locations of the reservoir, particularly where water is injected into the reservoir. This is expected based on the rock physics studies, which show that an increase in water saturation increases the acoustic impedance.

Summary All quantitative Seismic Reservoir Characterization workflows rely on careful seismic-to-well ties. It turns out that many Quantitative Interpretation (QI) workflows consist in linear combinations of prestack seismic data such as rotations / projections of Near / Far angle stacks or AVO intercept / gradient in their respective cross-plot domains. More sophisticated techniques such as prestack seismic inversion designed to estimate acoustic impedance and Poisson’s ratio to be further used for facies classification have a quasi-linear behavior relative to their input. Some of these combinations may reveal quite unstable so that good seismic-to-well ties of their input may not be sufficient to ensure quality results. It is proposed here to assess the quality of seismic input not only within its measurement range but also across a comprehensive range of linear combinations of the same input. Examples will illustrate that: -It may be hazardous to plug real data into a linear combination derived from synthetic data analysis only. -When scanning real data for an appropriate combination that matches a given elastic well log or even when using constrained seismic inversion results, the outcome might not genuinely represent the said elastic parameter.

When performing seismic inversion over a large area we often face lateral changes in the seismic signature not related to the geology of the target. Therefore a methodology to equalize seismic data over large areas was developed. The methodology was used to prepare the data for absolute seismic acoustic inversion. The P-wave impedance result was subsequently transformed into porosity via a relationship from well data. The methodology builds on the ability to assess differences in data using an un-biased wavelet and well tie estimation method. The method is utilized to correct for data issues without removing geologic information. We did this by constructing a matching filter from a wavelet parametrization that varied with depth. The methodology was successfully applied for regional porosity estimation over an area of 6400 km2, or more than 40 million seismic traces, in the Danish North Sea. The regional nature of the results is beneficial for the large scale geological understanding of the area especially for exploration purposes. Furthermore, it provides insights into relations between porosity and geomorphology, and helps identifying new exploration opportunities.

Summary “Based on pressure information obtained during drilling, the various shale units within the Wolfcamp Formation in the Delaware Basin are known to be variably pressured with depth, and the pressure can change laterally within the same rock formation. Zones with anomalous high pressure are generally linked to wells with better production rates. Unknown overpressured areas are also considered a drilling hazard and being able to predict these cells is of high interest. A pre-stack, facies-based seismic inversion process, capable of producing physical estimates of impedances and density, was used to invert the seismic land dataset for facies and elastic properties. The predicted elastic properties then honour the rock physics relationships observed in the well log data with greater fidelity. This is important when considering the subsequent geological characterization and geomechanical analysis using the property volumes. The well-based workflow was then applied to inversion results to generate a regional understanding of the distribution of pore pressure and geomechanical properties which allows for identification of sweet spots leading to more efficient and safer drilling. The predicted pressures and stresses match the magnitude of measured data and the vertical and lateral distribution of observed wellbore damage from DFIT and Image Log data.”

Summary Exploration and development of Jurassic sandstones on the southern and central NCS mainly focused on the Brent reservoir interval. The Skarfjell discovery to the west of the Vega field refocused exploration in the area to the Upper Jurassic strata. We present a case study from the Vega area of Q35 where recent oil discoveries have been made in so far underexplored Upper Jurassic deep marine turbidite sandstones. Oxfordian sandstones penetrated by the exploration wells were found to be strongly cemented and tight, but significant sand thicknesses and turbidite channel fairways have been mapped on seismic. In a rather difficult seismic imaging context and in the structurally complex Northern North See geology including tilted and inverted fault blocks, seismic inversion was successfully used as sandstone facies discriminator. A new play concept was established for Upper Jurassic Oxfordian reservoirs. A refined integrated seismic to geomodelling approach using the new exploration wells as constraint allowed to characterize reservoir quantity and quality for development evaluation purposes.

Investigating offshore groundwater flow provides an excellent opportunity to improve our knowledge on the global hydrogeological cycle. A fundamental way to understand the key factors in offshore groundwater flow is through numerical simulations using a realistic three-dimensional (3D) hydrogeological model of the New Jersey shelf. The work presented here comprises the application of acoustic impedance inversion to several two-dimensional (2D) seismic profile lines acquired along the shelf. The results provide new insights on the subsurface porosity distribution in that area and has served as a guidance for facies delineation in the hydrogeological model which we are currently constructing. The acoustic impedance section also enables us to refine the initial seismic horizon picks which are essential for improving the geometry of the hydrogeological model. The quantitative interpretation of the porosity distribution might as well open a new geologic perspective of the area, which so far has been only interpreted in more conventional and less deterministic ways. Additionally, the work presented here also serves as an assessment to perform more advanced rock physics methods for the groundwater reservoir characterization at the New Jersey shelf within follow-up projects.

The glutenite reservoir is widely distributed in China and has huge potential for hydrocarbon resources exploration. However, due to the near-source sedimentation and rapid facies transition mechanism, the glutenite reservoirs have strong heterogeneity with low porosity and complex pore structures, which makes it difficult to be predicted from seismic data. In this paper, we combine the Zoeppritz-based prestack seismic inversion method with rock physics analysis of glutenite reservoirs to characterize the oil distribution. Sensitivity analysis of different fluid indicators demonstrates that Poisson's ratio is effective to distinguish oil glutenite reservoirs from dry glutenite and shale formations. Application in Mahu slope area of Junggar basin shows that the oil glutenite reservoir prediction results are consistent with well log interpretation and production data, which verifies the effectiveness and accuracy of this method and can provide a significant guidance for the further exploration of glutenite hydrocarbon reservoirs.

Summary Black Sea Oil and Gas’ concession XVa Midia is on the Romanian continental shelf of the Black Sea which the company operates on behalf of its partners Gas Plus International BV and Petro Ventures Resources SRL. This study demonstrates how a suite of seismic processing and QI (Quantitative Interpretation) technologies was optimised to fully resolve gas assets currently under development and highlight satellite exploration targets in Miocene to Pliocene age (late Pontian to Dacian) fluvio-deltaic sands and claystones. Near surface channel features and localised gas presence causes seismic imaging issues as amplitudes are attenuated and time structural push-downs are observed. A 3D survey was acquired during 2016 and processed together with three legacy 3D surveys. This data was used for exploration well planning for a drilling programme of two wells Q4 2017. During the seismic processing tests were run to investigate possible imaging improvements using Full Waveform Inversion (FWI) and Q-tomography. A subset of the 3D was reprocessed using these methods during Q4 2017. This presentation will demonstrate the imaging uplift achieved and the impact on the interpretation together with pre and post drill results guided by QI based on pre-stack seismic inversion and facies/fluid probability classification.

Summary “Geophysical parameters’ distribution is an important information to constrain seismic inversion and interpretation. In this work, we present a theory of Collocated CoKriging (CCK) to estimate the distributions of subsurface parameters in an area with limited number of well logs. Due to the complexity of sedimentary facies, it will be a challenging mission to depict a distinct boundary of the interested parameter. Therefore, some modelling methods divide the study area into pieces to distinguish different sedimentary facies. The modified CCK and a novel modelling strategy are proposed to estimate the distribution of subsurface parameters without dividing the study area by introducing an additional dimension of variables into CCK. We demonstrate the application of an efficient strategy in estimating P-wave velocity and density with 5 well logs in the China South Sea. The proposed method overcomes the problem of introducing sedimentary facies into the calculation and the error points appear in the velocity model obtained by CCK. The velocity and density models show higher correlation with sedimentary facies. Therefore, the proposed method can be used as a pre-treatment tool to help researchers do a good job of seismic inversion and interpretation.”

The utilization of S-wave velocity has become an essential task in seismic reflection surveys for lithological properties of subsurface materials. In this research, we applied horizontal component of a common scatter point (CSP) gather produced in the application of the Equivalent Offset Migration (EOM) method to obtain S-wave wavefields. As an AVO analysis of both vertical and horizontal components of the CSP gathers estimates physical properties of subsurface reflectors, we use the full waveform inversion (FWI) technique to estimate the physical properties directly with observed waveforms. Our simple numerical experiments have shown high accuracy, the applicability, and the validity of our method. We would like to propose the processing of CSP gathers with FWI as a method of lithological interpretation of subsurface materials.

Summary The work presents the results of well data analysis and petroelastic modeling in saline clastic and carbonate sections of Eastern Siberia fields. The core data analysis indicates a significant effect of salinization on the porosity and permeability properies of rock, which affects the presence of reservoir in wells. Such deposits require an integrated study to the interpretation and analysis of well data. The degree of detail and reliability of the petroelastic model in salin conditions of clastic and carbonate rocks is determined by the input data: set of logs and its quality, core data analysis, etc. Modeling of elastic properties was performed with usage the model of effective media. There are models which allow to take into account different salt distribution in the pore space, depending on the rock wettability. The main feature of such models is that the shear modulus of pore fluid isn’t equal to zero. Taking into account that the target interval is represented by laminated rocks, the best choice for seismic reservoir characterization will be pre-stack geostatistical inversion algorithm. Effective thickness of reservoirs, porosity and volume of salt in pore space affect the reliability of seismic inversion reservoir characterization.

Summary The past twenty years have witnessed significant developments in the way that seismic data are used in oil and gas exploration and production. Arguably, the most important has been the use of surface seismic, not only to map structures in detail but also to infer reservoir properties from an analysis of seismic amplitude and other attributes. Improvement in seismic amplitude fidelity coupled with advances in the understanding and application of seismic inversion and rock physics have made quantitative description of the reservoir and risk evaluation not only a possibility but an expectation in certain geological contexts. To benefit from the superior quality of the VSP image over the surface seismic one, we extend the reservoir characterization workflow to borehole data and show its application to a 2D walkaway VSP data recorded in Saudi Arabia.

Summary The successful exploitation of unconventional resource plays requires an understanding of the subsurface geology, natural fracture network and present day stress regime. Each play has its own unique combination of these factors that control the response to fracture stimulation and ultimately impact productivity. New borehole datasets and interpretation techniques have provided methods to appraise target formations at well locations. However, seismic reflection data remains the most viable source of spatial information away from well locations. We describe an analytical geomechanical workflow that is constructed using elastic property volumes from a pre-stack seismic inversion. The seismic inversion used in the workflow is an innovative Bayesian pre-stack approach that simultaneously inverts for both impedances and facies. The inversion is grounded in a comprehensive well-based rock physics analysis of the formations’ elastic properties. The geomechanical analysis uses the seismic property volumes from the inversion to generate an analytical prediction of the horizontal stresses. The stress field is then solved around a vertical cylindrical borehole to make predictions of the fracture initiation pressure. The results can be used to understand fracture behavior - including stress anisotropy, initiation pressures, fracture barriers, and potential height growth – at any potential well location within the data volume.

Summary Low frequency seismic data down to 2/3 Hz is required if seismic inversion to absolute rock and reservoir properties is aimed. Presently about 3/4 Hz offshore and 4/5 Hz land seismic data are feasible using specially arranged receiver and source arrays. The missing low frequencies in these cases can be obtained using background models derived from the seismic interval velocity field without having to rely on models from well log data. Seismic bandwidth extending to high frequencies is also important in getting reliable rock and reservoir property estimations from seismic data. Influence of low and high frequencies in seismic inversion are illustrated with synthetic and field data.

In this presentation the new datasets from the offshore Kwanza and Namibe Basins will be shown and the pre- and post-salt sequences, facies and plays that these illuminate will be explained. Examples will be shown of pre- and post-salt imaging, together with our understanding of the reservoir facies and reservoir properties in several intervals. Additionally, the results of a preliminary quantitative interpretation (QI) study, constrained by a depositional sequence model and using pre-stack seismic inversion will be shown, illustrating the reservoir and fluid property information that the seismic data reveals.

Summary We present a data integration study for reservoir characterization, resulting in estimates of hydrocarbon saturation in place within a reservoir. The limitations of using only seismic data for detailed reservoir characterization are well recognized. Seismic data are well suited to resolving structure and some rock properties, notably porosity, but attempts to map fluids from seismic data may be misleading due to the ambiguity between fluid and lithology effects. It is well known that CSEM data are sensitive to the presence of hydrocarbons, but the diffusive energy propagation results in low resolution images. Thus, a careful combination of both types of data helps to overcome their individual weaknesses. In this paper, we outline with a dataset example how the CSEM and seismic data can be integrated together with limited rock physical knowledge in the prospect area to estimate the total volume of hydrocarbon in place.

Summary Frequency-dependent interpretation has the potential of better sedimentary sequence and facies analysis. However, traditional frequency band-pass filtering method suffers from truncation noise and only divides the frequency components of seismic data rather than generates true frequency-dependent stratum reflection data. In this paper, a new seismic frequency and phase changing method based on the seismic complex spectral decomposition and reconstruction is investigated. The process of this method contains two steps. Firstly, using a complex Ricker wavelet library decomposes the seismic data and generates complex time-frequency reflection coefficients, in which the phase information is included. Secondly, reconstruct to desired data through changing the dominate frequency of the complex wavelets and the phase of the complex coefficients to an expected frequency and phase. The synthetic example helps to demonstrate the proposed method and the real data example shows the effectiveness.

Porosity mapping in carbonate reservoir based on seismic inversion and multiattribute seismic analysis has been done. Data used in this research were 3D seismic data, well logs, and geological information. According to geological report known that hidrocarbon traps in research area were limestone lithological layer as stratigraphical trap and faulted anticline as structural trap. Model based inversion was generalized to the seismic data and yielded an acoustic impedance volume as a result. Impedance volume and seismic data were used as input in multi attributes seismic analysis to obtain total porosity volume. Total porosity was derived from density log and neutron porosity log. Interpretation was done by made a horizontal slice of acoustic impedance and porosity volume. Based on impedance and porosiaty maps known that limestone lithological streak between BRF horizon to top TAF consist of variety of limestone from tight to porous carbonate with its acoustic impedance value range 9000 – 11500 m.s-1.g.cc-1 and porosity value between 8 – 19 %. Distribution of carbonate layer in this research field was inclined to North-West direction.

In recent years, with the discovery of oil and gas in the deepwater area of the South China Sea, the huge exploration potential of this area has been confirmed. However, considering the low exploration level and the ghost problem in deepwater area, seismic reservoir prediction result has great uncertainty which makes the high-cost drilling at a risk. Therefore, it is urgent to develop a high-fidelity hydrocarbon detection technique without well constraints. In this paper, we proposed an integrated gas reservoir prediction method which combines seismic inversion with the frequency attribute based seismic liquid identification, and compared the different gas reservoir prediction results between conventional-depth streamer (CDS) and variable-depth streamer (VDS) seismic data. Application results show that the abundant low frequency information in VDS data is better for the interpretation and inversion of seismic data, and it can help identify the gas reservoir which cannot be recognized by CDS data.

Time-lapse or 4D seismic data is obtained by acquiring different 3D seismic surveys throughout the producing life of a hydrocarbon field. Due to the capabilities of 4D data to provide spatial information on the main reservoir changes due to production, time-lapse data has increasingly been used together with production data in the calibration of reservoir simulations models through 4D Seismic History Matching.

Summary In unconventional reservoir exploration, fractures are important space for hydrocarbon. Fractures can be treated as an interfaces developed in host rocks. An approximate equation of P-wave reflectivity of fracture interface is introduced and corresponding pre-stack inversion is performed. The new inversion method can estimate fracture and host rock properties simultaneously, which can be used in further reservoir evaluation.

Time-lapse or 4D-seismic refers to the repetition of a seismic survey after a period of time, time being the ‘fourth dimension’ in an effort to image changes that could have occurred in a reservoir (Tran, et al., 2003). The time-lapse method is useful in that reservoir changes between wells can be detected. It can also be used to improve production, by identifying areas of bypassed oil. The seismic response of a reservoir may change due to changes in pressure, fluid saturation and temperature (Landro et.al., 1999). The changes in seismic images due to a variation in saturation and pressure can be used as additional observation data. Time-lapse seismic data are dynamical measurements which have a high resolution in the horizontal direction, and give a significant image of fluid and pressure changes for the entire reservoir.

Summary Seismic data can be considered as the convolution between reflection coefficient and band limited wavelet. The usual wavelet is band limited, whose main lobe has the long time and side lobe energy is strong. Therefore, seismic signal has the serious interference and low resolution. The main lobe of wide-band Ricker wavelet (also called Yu’s wavelet) is narrow and side lobe amplitude is small. Due to the seismic data with wide-band Ricker wavelet’s spectrum characteristics, the interference effect is weak, which is more suitable for reservoir description. In this paper, the spectral inversion method based on the geological statistics is used, and the spectral inversion technique is improved. We can get the broadband data with high frequency and low frequency, and then the broadband data is transformed to the seismic data with Yu’s wavelet spectrum characteristics in order to improve the accuracy of reservoir description. The method in this paper is practical applied to real oilfield in Bohai sea, and the improvement of seismic data is fitter for the reservoir description, which shows the proposed method is feasible and effective.

Summary Fracture fluid indicators (FFIs) can be used for fluid identification in fractured reservoirs. Elastic impedance (EI) inversion is a stable and efficient estimate of reservoir parameters. Rock-physics models build the bridge between the seismic response and the reservoir parameters and hence contribute to the seismic inversion and fluid identification in fractured reservoir. In this paper, we proposed a new FFI based on azimuthally anisotropic EI inversion and fractured anisotropic rock-physics models. First, we derived a new azimuthally anisotropic EI equation. Then, we built a fractured anisotropic rock-physics model used for the estimation of well-log parameters for the normal and tangential fracture weaknesses, which builds the initial background low-frequency trend of fracture weaknesses. Finally, based on the azimuthally anisotropic EI inversion method, we estimated a FFI applied to fluid identification in fractured reservoirs. A test on a real data demonstrates that the anisotropic parameters related to fracture weaknesses can be estimated reasonably and stably, and our method appears to provide an alternative available for fluid identification in fractured reservoirs.

Summary Seismic inversion is the process of removing the effects of propagated wavelet to recover the values of acoustic impedance. To estimate a reliable mixed-phase wavelet for a viscoelastic medium, we utilized higher-order statistics method. Non-minimum phase wavelet estimation based on cumulant matching is known as statistical wavelet estimation approach. To apply this approach, we windowed the seismic data in such a way that two consecutive windows were only one sample apart. No fixed wavelet was considered for any windows and we let the phase of each wavelet to rotate at each sample in the window. Comparison of the fourth-order cumulant of the whitened trace with fourth-order moment of the all-pass operator in each window yields a cost function which should be minimized. In this regard, we chose simulated annealing method. The proposed method was applied to synthetic and real data set from one of hydrocarbon fields of Iran. The results showed that the proposed method can estimate non-stationary mixed-phase wavelet in area with variable Q values and low signal-to-noise ratio. The highest correlation is about 89%.