To improve the accuracy of permeability prediction, seismic constraint and sedimentary facies has often been adopted in conventional methods. However, it is porosity that both of them constrain, rather than permeability, and different pore structure with different permeability, the accuracy of permeability prediction cannot be radically improved. To address the problem of permeability prediction in carbonate reservoir, new permeability prediction technique workflow were summarized based on pore structure analysis and multi-parameters seismic inversion: division reservoir types based on the pore structure, construction of the rock types identification curve, carry out a rock type inversion and a porosity inversion constrained by seismic impedance respectively, and then get a final permeability prediction volume according to the porosity-permeability relationship and pore structure of core samples. It breaks the bottleneck that is difficult for seismic impedance (continuous variable) to constrain rock type (discrete variable), then constrains pore structure (continuous variable) related to rock type instead, and converts it into rock type using multi-parameters seismic inversion. According to the certification of new wells, this workflow have been applied successfully in carbonate reservoir of H oilfield in Middle East, it not only improves the prediction of rock type in space, but also permeability prediction accuracy.

A heavy oil field is developed with the Steam Assisted Gravity Drainage (SAGD). Since the start of oil production, the oil production amount of the studied reservoir has been lower than that of the surrounding oil fields. The development of the steam chambers are relatively poor, and the spatial distribution of the steam chambers is not clear. In order to describe the shape of the steam chambers accurately, a novel method of monitoring the steam chambers is proposed by using broadband, high-density seismic data and dynamic production data. Broadband, high-density seismic data are acquired. Firstly, seismic horizons are well interpreted, and multiple seismic attributes are analyzed in combination with well data to understand the seismic response of the steam chambers. Then, several seismic inversion methods are tried to obtain high-resolution impedance, and they are compared with each other to find optimal inversion result for steam chamber prediction. Finally, the shape of the steam chambers are delineated with seismic attributes, inverted impedance, production data, and temperature logs of observation wells. The predicted steam chamber has good agreement with the temperature logs of observation wells. The result shows that the appearance of steam chambers has an obvious impact on seismic signals, and broadband, high-density seismic data make great contributions to the description of the steam chambers. The proposed method reduces the uncertainty in steam chamber prediction with single data source, and is applicable to other oilfields using steam injection.

The purpose of this paper is to highlight the similarity between Connected Reservoir Regions (CRR)map created using time-lapse pressure groups ( Kayode et.al 2018 )and other reservoir quality maps like Seismic Acoustic Impedance (SAI) map and petro-physical rock quality map. Time-lapse average reservoir pressure from producers and injectors spanning several years of field production were sorted into groups of similar pressure trends. Wells that show similar pressure trend were classified into same CRR, while wells that show different pressure trends were classified into different CRRs. Only wells operating within the same reservoir zone have been used in the pressure grouping in order to ensure that the observed pressure trend differences are only due to lateral variations of reservoir quality and not due to vertical zonation. A geo-modelling software was used to create connected reservoir regions map in which all wells within the same pressure group are identified with a unique colour code and polygons are drawn to delineate the spatial limits of wells within each pressure group. The CRR map thus obtained, was then compared with SAI map and permeability quality map. Similarity was observed between the CRR map, SAI map and petro-physical rock quality map. Areas indicated as poor quality (high impedance) on the SAI map and indicated as low permeability on petro-physical map were consistent with CRR regions that are characterized by high injection pressure and poor pressure support. Areas indicated as good quality (low impedance) on SAI map and high permeability on petro-physical rock quality map were consistent with CRR regions that are characterized by low injection pressure and excellent producer-injector communication. In addition, a particular well was sidetracked in order to improve reservoir sweep, this producer whose pressure had been historically fairly steady, experienced a sudden increase of time-lapse average reservoir pressure. When the pre and post sidetrack locations of this well were plotted on CRR map, the reason for the sudden pressure increase became obvious; well was sidetracked across CRR boundary, from a poor reservoir quality to a good reservoir quality CRR. In certain cases, oil and gas fields may not have seismic data, in other cases the resolution of the returned seismic signal may be weak. In such cases, CRR maps created using time-lapse average reservoir pressure groups could be used during geo-modelling,for controlling the distribution of 3-D properties away from well control points, instead of seismic acoustic impedance reservoir quality map.

A series of light oil fields which are characterized by shallow burial, good physical property have been discovered through latest exploration in Baiyun Depression of Pearl River Mouth Basin. However light oil reservoir is "Atypical bright spot" reservoir. The light oil-bearing and gas bearing structures in the seismic section are characterized by "bright spot" reflection, and the difference of elastic parameters is not obvious. Traditional methods are inefficient to discriminate oil, gas and water. Aiming at the above problems, the fluid rock physical interpretation version is rebuilt through the analysis of Gassmann fluid factor based on two-phase medium petrophysics theory and the elastic parameters of the P-wave impedance. Then the three fluid properties of oil, gas and water are classified by direct inversion technique based on elastic impedance. The real data processing and exploration practice have proved the feasibility and validity of this method which is instructive to deep water exploration.

Before undertaking any seismic interpretation project, it is essential to understand the rock physics of the area so that the interpreter knows what to look for in their seismic. Rock physics modelling can be used to predict the effects of lithology, fluid, porosity, depth and sand thickness on the seismic response. It can also help to understand whether direct hydrocarbon indicators (DHIs) such as flat spots, bright spots, dim spots or polarity reversals are expected and also the best seismic attributes to use to identify hydrocarbons. Well ties can be used to determine the quality of the seismic data and infer how reliably predictions based on the rock physics can be applied back to the seismic data. The rock physics of several producing assets across the Gulf of Thailand have been examined and compared. Rock properties of over 200 wells have been analyzed based on stratigraphy, lithology, fluid fill, facies, porosity and depth. It was observed that the rock physics could be split into two distinct regimes based upon depositional environment separated by a key unconformity. The first rock physics regime is observed in the shallow high porosity fluvial systems found within the Nong Yao Field and the younger reservoirs of the Jasmine and Manora Fields. In this regime, seismic attributes and seismic inversion have been successfully used to predict sand presence and thickness. Sands and shales have distinctly different rock properties and sands produce a strong class III-IV Amplitude Varation with Offset (AVO) response. Fluid effects can be observed on seismic although it is difficult as thickness and porosity changes may be incorrectly interpreted as fluid changes. The second rock physics regime is observed in the older, lower porosity lacustrine system found within the main reservoirs of the Manora Field and the pre-MMU reservoirs of the Jasmine Field. Within this regime, lithology prediction is much more difficult as sands and shales have very similar properties. Sands are modelled to have a class I-II AVO response and fluid effects are minimal. Understanding the rock physics of these fields allows future work to be focused appropriately. At the Nong Yao Field and in the younger section of the Jasmine Field, technical work is focused on seismic inversion, seismic attribute analysis and other geophysical techniques for lithology and fluid prediction. Whereas at the Manora Field and in the older reservoirs of the Jasmine Field, technical work is focused more on structural mapping and geological modelling, and amplitude analysis is of lower priority.

The success of extracting the best value from mature heterogeneous fields to meet global energy challenges is directly linked to innovation and creativity. The development of these fields requires optimized economic models and fit for purpose reservoir depletion strategies. Petroleum geoengineering is the answer to evaluating remaining opportunities and managing the key uncertainties using smart technologies and reducing the risk of development. This paper describes the field example of how a petroleum geoengineering based approach can optimize the fast track development of a marginal fault block within a complex oil field which is located in offshore Sarawak Malaysia. The advantages of this workflow compared to the conventional field development plan (FDP) approach is that the iterative time consumed in matching the dynamic model and adjusting the geological model has been properly managed. In our case study, this workflow has improved the quality of the technical proposal as well as saving project duration significantly. In accordance to the workflow, the reservoir architecture was interpreted based on seismic interpretation, geological and reservoir performance understanding. Seismic Inversion derived geobodies were then identified as development targets. Better delineation of the main geological features was achieved using a seismic inversion based algorithm. By prioritizing data consistency among geoscientists, geomodellers and reservoir engineers, a fast track field development plan was developed within manageable uncertainties. Greater reliability of inversion results help to avoid perpetuating bias tendencies on data used for model calibration or quality check. The petroleum geoengineering based study for such oil reservoir shows how an integrated approach enabled time saving for subsurface development concept identification covering range of estimated hydrocarbon volumes. As a result of geoengineering approach in marginal fault block, the technical proposal was completed within 8 months duration.

Cement Bond Logs (CBL) and/or Cement Evaluation Logs are usually conducted to evaluate the quality of the cement bond behind a cemented casing or liner before a well test or production operation is performed in the well. These logs may also be carried out during workover operations to evaluate the integrity of the casing before sidetracking the well. In many countries, regulatory authorities require that cement bond logs be conducted in every well after cementing operations to investigate the presence and quality of the cement bond behind the casing. This is performed to evaluate the hydraulic and mechanical seal of the cement. There is still a prolonged discussion in the industry regarding the validity and reliability of such cement evaluation logs in determining the quality of the cement bond behind pipe. There is a faction which believes in the cement evaluation logs and uses them regularly while another does not believe in these logs and hence do not utilise them at all. The CBL, which records the amplitude and transit time of a sonic signal travelling along the casing, has been around for many decades. The measurement principle of the CBL tool is well known and understood. The new generation cement evaluation tools, such as the ultrasonic scanner tool, use an ultrasonic transducer to send an ultrasonic signal inside the casing and estimate the acoustic impedance of the cement behind the pipe. The measurement principle and processing of such a cement evaluation log is more complicated and can be sometimes difficult to understand. Furthermore, depending on the input parameters used, such as the impedance of the wellbore fluid, a cement evaluation log can give contradicting results. For example, a log which has been processed using a certain mud impedance showing very poor cement quality can be reprocessed with a different mud impedance and produce another log which indicates better cement quality. Therefore, it is important to understand how such a log is processed before making a decision to take any remedial action based on the apparently poor quality of the cement bond indicated by the log. In this paper, ultrasonic cement evaluation log examples illustrate the complex task of evaluating the quality of the cement bond. Likewise, even the old CBL can be affected by the properties of the borehole fluid. A heavy mud in the well can attenuate the sonic signal, leading to spuriously low CBL amplitude and hence falsely indicating a good cement bond. Cases like micro-annulus, if not properly recognised, will be interpreted as poor cement bond and may lead to unnecessary and unsuccessful attempts to squeeze cement into the annulus. The presence of a micro-annulus can be determined by re-running the CBL log with the casing under pressure. The case histories in this paper illustrate the importance of understanding how the cement bond and evaluation logs are acquired and processed, so that proper decisions can be made regarding the quality of the cement bond for any remedial action that needs to be taken. Examples are also given to illustrate the instances where total disregard of cement bond and cement evaluation logs can result in undesirable consequences leading to expensive remedial actions later. This paper presents case studies which will highlight the importance of evaluating cement bond quality behind pipe. It will indicate that cement bond and cement evaluation logs play an important role in identifying possible well integrity issues, which can result from poor cementing jobs. Therefore, due diligence and care should be taken in interpreting cement bond and cement evaluation logs to prevent costly mistakes.

Extended Abstract The study area is located in the shallow water of Tenggiri Basin, a region where numerous gas fields have been discovered and which has a great deal of remaining potential. In order to fully evaluate the area a new broadband seismic dataset was acquired and an integrated subsurface study was initiated. The goal of the study was to evaluate the discovered fields and new development area for prospective hydrocarbon potential. Furthermore the study sought to gain deeper understanding of the stratigraphic architecture and insight into how to optimally develop these complex reservoirs. This study consisted of a detailed integration of modern broadband seismic data, rock physics, AVO, attribute studies and seismic facies classification. The reservoir characterization study has improved our understanding on the lithological distribution and increase the confidence of the established depositional model for the Miocene reservoirs. Introduction The Miocene reservoirs of Tenggiri Block are challenging to characterize as they are arealy discrete, close to or below tuning and geologically complex. The reservoirs are interpreted to have been deposited in a low relief fluvial-estuarine to shallow marine setting. This paper documents three intervals, presented in order of increasing geological and geophysical complexity, named: Miocene C-10, Miocene C-20 and Miocene B-10. All 3 intervals are penetrated by 4 existing wells. The wells contain an excellent data base including checkshot survey and shear logs; for the generation of synthetic seismograms and AVO modeling. The block is fully covered by a recently acquired modern broadband 3D seismic survey shot in 2012 and processed through VTI anisotropic Pre-Stack Depth Migration in 2013. The study area has significant occurrence of coal and carbonaceous shale throughout. Coals and carbonaceous shale can mask bright amplitude hydrocarbon reservoirs and the effect is especially prominent for thin bedded reservoirs. Interval C-10 is characterized by NW-SE trending channel system penetrated by one well with approximately 50 feet of gas sand. According to wedge modelling 50 feet is approximately close to tuning. Interval C-20 is another adjacent event situated directly below Interval C-10, none of the existing wells have penetrated any of the prospective seismic anomalies. Based purely on available seismic extractions, it is suggested that Interval C-20 shows smaller channels also trending NW-SE while the majority of the southeastern area is covered with coal. Additionally Interval C-20 appears to contain a perpendicular NE-SW channel system that is interpreted to be shale-filled. Based on well data, Interval B-10 is characterized with moderate quality thin sands intercalated between the presences of laterally extensive coal layer and carbonaceous material patches. Interval B-10 has many channel-like features however they are significantly more esoteric and lack the obvious orientations of the C-10 and C-20 Intervals.

Abstract The transformation of seismic reflection data into quantitative rock properties can be very valuable in all phases of oil and gas exploration and production. In this study, public domain data from Australian offshore have been used to explain the logical workflow, which consists of systematic steps from seismic petrophysics to rock physics modeling and seismic inversion for quantitative rock property estimation. The application of seismic inversion technology offers many rewards, such as: better reservoir definition and management; better resource estimation; and reductions in uncertainty. The main benefit is that it improves direct and intangible interpretation of seismic data to provide meaningful geological boundaries in the subsurface. In order to provide the reliable estimate of rock properties for reservoir modeling the workflow is tested rigorously and divided into three categories which are based on the basic inversion types. The results reveal that relative impedance inversion should be performed first, which then followed by revision of seismic interpretation on impedance data. Full bandwidth rock properties are estimated by deterministic inversion. The results can be improved by several iterations of well to seismic tie, wavelet estimation, and low frequency models. As resolution with accuracy has always been challenging, the stochastic inversion approach is trailered for reservoir characterization and high resolution rock properties have been predicted. This produces a number of possible rock property models, as well as litho facies models and the results can be optionally constrained by the well data. It can be concluded that, to some degree, stochastic inversion is able to overcome the limited seismic bandwidth by integrating the rock physics properties of different lithologies. The examples of seismic inversion are discussed to demonstrate the significant benefits can be obtained by following an optimized workflow that is tailored to deal with the uncertainties that impact the end product. Introduction Seismic surveys are routinely performed to gather information about the geology of hydrocarbon fields. Seismic data, even when it has relatively low resolutions, can still deliver a structural model for the reservoir. The availability and coverage area of the data provides valuable information to constrain the reservoir model. Geophysicists are now able to extract greater quantitative information about reservoir rocks and fluid parameters from seismic data by working backwards using seismic inversion methods. Described as the flip-side of forward modeling, seismic inversion is the process of characterizing rocks and fluids that have produced the seismic data. In a sense, the process involves trying to determine the input (rock properties) by looking at the output (seismic data). It starts with actual measured data, removes the wavelet and derives acoustic impedance from reflection coefficient (Figure-1) with a low frequency model to infer rock properties and deliver earth model.

Abstract Stochastic inversion of 3-D seismic data is used increasingly for reservoir characterization. It provides information on the reservoir at a much finer scale than deterministic inversion and delivers multiple scenarios for uncertainty analysis. In this work, a stochastic seismic inversion workflow has been developed to characterize porosity variations in the Thamama reservoir of the giant Field, onshore Abu Dhabi. In contrast to traditional band-limited inversion, this stochastic inversion workflow first generates high frequency models of acoustic impedance which can be used directly for geomodelling without downscaling. Next, a collocated co-kriging sequential Gaussian simulation technique has been applied to generate fine scale 3-D porosity realizations constrained by the impedance stochastic models and by log porosity data. In the example, post-stack stochastic inversion is combined with stochastic porosity modeling to characterize the uncertainty in the spatial distribution of thin, low porosity / permeability intra-Thamama layers, which adversely affect the field water flood performance. These thin layers have been mapped using seismic-constrained stochastic workflow. P10, P50 and P90 porosity realizations have been generated which represent more or less pessimistic scenarios of lateral extent of the tight zone along the flank of the field. A number of blind wells demonstrate that the seismic-based workflow provides more accurate porosity predictions than a purely well-based reservoir model. Specific technical contributions of the work include: Demonstrate the value of seismic information for characterizing mature carbonate reservoirs Implement field-specific stochastic workflow to characterize uncertainty in spatial extent of thin reservoir flow barriers from 3-D seismic data Perform seismic inversion directly in fine-scale stratigraphic grid to facilitate integration with the field geomodel

Abstract The Lower and Middle Ordovician carbonate karstic reservoirs form an important type of reservoirs in the Tarim basin, the largest inland basin in China. Karstic feature characterization along with infill identification provides a great opportunity to delineate the distribution and connectivity of collapsed-cave systems. Based on carbonate karst concepts, this paper presents an integrated approach to characterize collapsed-cave systems and to map the geomorphology and distribution of karstic drainage components. Seismic amplitude and geometric attributes are used to identify karst features such as collapsed-cave complexes, conduits and infiltration or dissolution zones. A multivariate attribute classification technique is applied to generate karst seismic facies that highlight these features. Clustered karst features are sampled into the grid model to construct a 3D architecture model of collapsed-cave systems. This model eventually incorporates all clustered features, revealing their spatial distributions, inherent complex shapes and lateral connectivity. In the case of preserved collapsed-cave systems become disconnected and occluded as a result of infilling and roof collapsing, collapsed-cave systems need to be further calibrated in order to locate infill drilling and identify dynamic compartments. The seismic acoustic impedance attribute facilitates the identification of infill within collapsed-cave systems because chaotic breakdown breccias and cave-sediment fills have an impedance lower than that of the host limestone resulting in a significant impedance contrast. Incorporating the impedance attribute with the architecture model, the bodychecking technique is applied to searching for connected cells to extract karstic drainage components. The geomorphology and distribution of individual components can be mapped. Integrating reservoir production data and borehole imaging logs, the drainage components can be evaluated and sorted. The case study is addressed to illustrate applications of these technologies and their efficiency. Introduction Karst systems in the Tarim basin are formed by subaerial exposure and karstification during a long period of time from the Lower to the Middle Ordovician. Loucks (1999) showed that most karst reservoirs are not products of isolated collapsed passages, but instead are a product of coalesced collapsed-paleocave systems hundreds to several thousand meters across and thousands of meters long. Identifying karst related products in cores, i.e. cements and fabrics, dissolution porosity and specific features, can help to recognize and describe karst systems. The origins of coalesced, collapsed karst systems and associated product features have been studied (Kerans, 1988; Loucks and Handford, 1992; Loucks, 1999; Loucks et al 2004; McDonnell et al, 2007). Sags associated with collapse were good indicators to locate the coalesced collapsed carbonate Karsts (Lucia, 1996). Loucks et al. (2004) defined the three-dimensional, interwell-scale architecture of a Lower Ordovician Ellenburger coalesced, collapsed-paleocave system through the integration of ground-penetrating radar (GPR), shallow-core, and outcrop data. Karst systems have complex features and tend to have pronounced lateral and vertical heterogeneity, especially when multiple phases of karsting and burial are involved (Loucks and Mescher, 1997; McMechan et al., 1998; Loucks, 1999).

Abstract The use of extended elastic impedance (EEI) as described by Whitcombe et al. (2002) gives promise of a fast and objective way to relate seismic inversion results to different reservoir properties such as, e.g., porosity, fluid content and lithology. This can be a useful tool for exploration, prospect evaluation, and early reservoir characterization. At the same time it is generally less time consuming than other methods such as simultaneous AVO inversion combined with rock physics inversion or lithology/fluid facies classification studies. Two key elements in the EEI workflow are to find the correct chi angles projections used for relating the EEI to different elastic properties and to estimate and select the right wavelet for the inversion of the 0° and 90° EEI. Herein, we will show how rock physics modeling, fluid substitution, and the selection of input data can be crucial to obtain quality results and also in situations where modeling will not be necessary. In addition, we will demonstrate how the EEI inversion workflow can be constructed to be time efficient and to provide quality results. An offshore exploration example will illustrate the possibilities and results with this workflow, where the results were used in prospect evaluation, de-risking, quantitative interpretation, and separation of brine vs. hydrocarbon-saturated sands. Introduction The techniques for prestack seismic inversion have envolved from generalized linear inversion (Tarantolla, 1986), elastic inversion (Connolly, 1999) to simultaneous inversion (Ma, 2002). Often the focus is to get the best possible quality (e.g., simultaneous inversion) or small scale details and uncertainties (stochastic inversion). Even though efficient inversion algorithms and powerful computers have reduced the computation time, the time required for data preparation and interpretation seems to be harder to reduce. However, for some reservoirs and situations, the critical aspect might be how to quickly obtain quantitative interpretation for prospect evaluation, or to limit (and understand) the amount of subjectivity influencing the results. These might be some of the reasons why the EEI method (Whitcombe et al., 2002) has recently gained some popularity. The method can be used for direct indication of or quantification for hydrocarbon saturation and various other reservoir properties, and it utilizes the intercept and gradient as input to the inversion. In this work we discuss how this technique can be used in an efficient inversion workflow and how to relate the inversion results to reservoir properties. We will also discuss some pitfalls and how to avoid them. In the two-term Zoeppritz linearization (Aki and Richards, 1980) given in equation (1), A refers to the normal incidence reflection coefficient (often called intercept), B refers to the variation at intermediate offsets (often called the AVO gradient, or the gradient), and ? is the angle of incidence. The extended elastic impedance as described by Whitcombe et al. (2002) is an extension of the elastic impedance, where, first, the sin^2 ? term is replaced by the term tan ?.

Abstract The studied reservoir, a large mature offshore Middle Eastern field, producing from Upper Mishrif limestone series, shows strong heterogeneities (vertical and lateral), due to the original properties of the sedimentary facies and the effect of several subsequent phases of diagenesis and fracturing. This results in complex dynamic field behaviors, which can be difficult to predict including strong saturation variation in the long transition zone, localized dual porosity/permeability flow, early water breakthrough, strong water-cut and uneven/non-uniform pressure support. In order to better predict & optimize future production and/or support additional developments, a robust reservoir model integrating all available data (geological, geophysical and dynamic) is clearly needed. This paper focuses on two key steps in geo-model construction: facies and porosity modeling and more specifically on the use of seismic data for reservoir characterization while taking care of over-utilization. Aims are to improve model reliability and prediction capabilities. Facies and porosity modeling was constrained by numerous data from cored exploration vertical wells and horizontal development wells. Initially reservoir facies represented by eight "electro-facies" are distributed according to the available well data and proportion maps derived from regional knowledge. Then porosity is populated within each facies with dedicated distribution and honoring well data. In parallel, a 3D pseudo-porosity cube was generated by applying the strong linear correlation observed between acoustic impedance and well porosity to an acoustic impedance dataset resulting from a post-stack inversion. It serves as an additional large-scale constraint for modeling: the vertical resolution of the pseudo-porosity cube is roughly 15 meters, while facies and porosity changes occur vertically within 3–8 meter thick layers as described from the well data, it extends over most of the field. It is integrated (1) as a direct soft trend for porosity and (2) as qualitative check of the final geo-model. Once porosity is modeled; it is converted into pseudo-impedance and a synthetic seismic dataset for comparison with original data. In addition the pseudo-impedance was compared to the acoustic impedance cube resampled at reservoir model scale. Both allow significant seismic-scale inconsistencies to be easily detected and understood for further iterative correction. Once areas and origin of mismatch between seismic data and the geological model were identified, reservoir model porosity, and consequently facies, had to be locally modified to ensure consistency with geophysical information. The modified reservoir model porosity was then used to regenerate a synthetic seismic data and compared to the original seismic data in order to check if the modifications were sufficient to obtain optimum coherency between all data. This kind of feedback loop is an efficient iterative process, a time-consuming but necessary step for relevant geo-model quality control. The consistency between all available geological and geophysical information gives high confidence in the result, and ignores seismic noise. It ensures the robustness of the geological model and consequently ensures a sound basis for dynamic modeling.

Abstract The Shuaiba reservoir of the Al Shaheen Field (Block 5 and Block 5 Extension, offshore Qatar) can be subdivided into a larger carbonate platform area located in the NE of Block 5 and an intra-platform basin (southern part of Block 5) that was filled in by a forced regressive wedge (FRW) system during the Upper Aptian regression. The FRW is characterized by clinoforms with a marly base that act as intraformational baffles or seals. Capturing these 3D geometries in detail, and specifically the exact location and nature of the platform-basin transition, is essential to developing the next generation geomodels for field development. Hence, an integrated study of core and log analysis, seismic forward modelling and seismic interpretation of a re-processed data cube was conducted. As a start, a field-wide correlation grid of available core and log data allowed for an initial coarse understanding of the depositional geometries and defined an approximate location of the platform-basin transition. As a second step, the expected depositional geometries were forward-modelled in order to better understand possible seismic responses within the bandwidth of the available 3D seismic survey. A complication is the relatively small thickness of the examined reservoir elements that are close to the limit of vertical seismic resolution. Finally, seismic interpretation picked up on this groundwork and it was possible to clearly map the platform margin, as well as the onset of the FRW in 3D. The obtained result was a detailed 3D seismic interpretation of the Shuaiba reservoir geometries, where the interpreted elements are tied with a grid of core and log interpretations. The key elements in the successful Shuaiba interpretation was early adaption of fully integrated and iterative geological and geophysical workflows where forward modelling of seismic responses was important for hypothesis testing. Introduction The Shuaiba Formation is amongst the most important reservoir units in the Middle East. Its layout on the SE Arabian Peninsula has been extensively studied over the years (e.g., Murris, 1980; van Buchem et al. 2002; Droste and Van Steenwinkel, 2004) and a regional sequence stratigraphic synthesis has been established by van Buchem et al. (2010). Yose et al. (2006; 2010) and Droste (2010) published seismic cross-sections from the U.A.E and Oman, respectively, which document the internal depositional geometries of the Shuaiba Formation in great detail. Capturing these 3D geometries, specifically the exact location and nature of the platform-basin transition, is essential for developing a robust structural framework as input to geomodelling. The Shuaiba Formation of Al Shaheen Field, Block 5 and Block 5 Extension, offshore Qatar, fits well into the regional sequence stratigraphic framework and thus comparable internal depositional geometries are to be expected. However, the interval in question is about 30% thinner than in many of the published examples (about 300 ft in Al Shaheen compared to 500 ft feet reported in the U.A.E.; Yose et al., 2006; 2010), which makes it difficult to interpret in the seismic realm. This is because the relevant stratigraphic building blocks are close to, or at the vertical resolution limit of the available 3D seismic data set. Seismic interpretation this close to the resolution limit calls for non-standard techniques. The focus of this paper is on documenting the integrated approach that was developed to successfully map the depositional geometries of the Shuaiba reservoir of Al Shaheen Field and presenting the resultant key outcomes.

Abstract The Arabian Gulf, in Saudi Arabia, is a mature petroleum province. As exploration moves from structural concepts to the stratigraphic domain, identifying petroleum system elements in areas with limited well control becomes imperative to cost-effective exploration. Explored areas are blessed with reliable datasets (most significantly well data) that records subsurfacerock properties. In exploratory areas with very sparse well control, seismic is the most appropriate data set currently available for approximating rock properties. Therefore, finding reliable seismic attributes that correlate to rock property variations in well logs holds the key to exploratory success. This paper presents the results of a project undertaken to identify seismic attributes that differentiate between source and reservoir facies within the carbonate Hanifa Formation in the Eastern Province of Saudi Arabia. A detailed analysis of wireline logs from wells suggests that rock properties like velocity, impedance, Lambda Rho and Mu Rho are higher in carbonate source facies under consideration than their reservoir counterparts. Impedance computed from seismic inversion is found to be in consonance with that derived from wireline log data. Projected facies are found to fit predictions of the geological model in wells in the study area. Mapping of impedance in the 3Dseismic volume helps carry the interpretations from the known to the unknown. Acoustic impedance, derived from seismic inversion, thereby offers promise as a tool for identification of petroleum system elements in areas with very sparse wells.

Abstract Four lithologies were developed in the study area and they were pore-bearing grainstone, compact sparite, shaly limestone and limy mudstone. The pore-bearing grainstone was the main reservoir rock. The characteristic analysis of the electrical and physical properties showed that the acoustic impedance of the pore-bearing grainstone was medium to relatively low. If we only use the inversion data of acoustic impedance, it is difficult to distinguish between the pore-bearing grainstone and other lithologies. In addition, it is difficult to distinguish the reservoir using any single given electrical parameter such as GR, density and resistivity. Therefore, we selected several sensitive logging parameters through crossplot analysis. On the basis of the geostatistical inversion, several volume data were combined, thereby the favorable reservoirs were determined. The multi-parameter geostatistical inversion integrated and constrained by logging data and seismic data. It not only takes the advantage of the geostatistical inversion which does not simply use the acoustic impedance to joins the calculation, but it also uses the curves such as GR, porosity which directly reflect the lithology and physical properties of the formation, to join the calculation. Compared with the conventional acoustic inversion, it greatly improved the vertical resolution, finely characterized the distribution of the reservoirs, and better conquered the problem of the multiplicity of the reservoir prediction and resolution?

Abstract In this article, the author introduces a time-lapse seismic inversion method based on the difference of seismic data at different times. Based on the synthetic model bSuilt from the real data, pore pressure change SSSSin a gas reservoir is computed. Also we predicted the reservoir pressure variation for a China offshore gas field as a real case study. Model experiment and real data application to describe the gas pressure variation show that this method is practical and efficient. It is well known that time-lapse seismic has been taken as a powerful tool for reservoir surveillance. With the widespread application of 4D technology, many attentions have been paid on the quantitative prediction of reservoir variation via time-lapse seismic data. Through time-lapse seismic prestack elastic impedance inversion, many elastic parameters variation can be obtained. Hereafter we can setup the statistic relationship between reservoir parameters variation and elastic parameters variation. Thus reservoir parameters variation can be estimated. There are two methods to achieve time-lapse seismic elastic impedance inversion process. One method is to invert each seismic data at different times separately, and then the impedance difference can be obtained from two inverted impedance results. This way is the general inversion scheme which is available in traditional commercial software. The other method is to firstly compute the difference of time-lapse seismic data at different times, on the base of which the impedance difference can be obtained by inversion directly. In this article, the author introduces the workflow and technology on how to conduct the inversion with the second method mentioned above. Model experiment and real data application have been done with both methods to compare the results and the advantage of each method. The comparison between the computation results and reservoir numerical simulation data shows that the method of inverting the difference of time-lapse seismic data directly is practical and efficient. It can reduce the computation amount greatly; simplify the computation process and enhance the precision. Introduction As we all known, with the development of the oilfield, the low rate of recovery has been the key problem affecting the profit and decision making on further development. Residual oil has been taken as great resource in recent years, but the complex underground structure and complex reservoir condition cause the difficulties in development scheme. How can we know the distribution of the remaining oil and reservoir parameters such as reservoir pressure variatiozn at different times of development? Time-lapse seismic is one of the technologies helping to understand reservoir. Based on the basic principle of the reservoir production, reservoir variation caused by the production can induce the change of seismic reflection characteristics around the pay zone. By comparing this difference, we can identify the change of the fluid, understand the distribution of the residual oil, therefore achieve the dynamic monitoring of residual oil and finally increase the recovery factor.

Abstract This middle cretaceous reef build up reservoir has similar problems to other fields of similar setting in the region, regarding upper contact in the form of Karstification with a mixture of fill, shally interbedding, lateral / vertical tight intervals, uneven Vug distribution and large transion zone. Those have presented a great challenge to adequately place horizontal wells, where near surface well path will intercept the Karst topography extending in the reservoir with its potential drilling hazard while away from the surface wells will end up in high SW zone. In this context, well placement becomes a real challenge. Few wells had to be plugged and side tracked adding several million dollars to the drilling cost beside the lost in hole equipment. After our 3D OBC seismic survey, the team attempted and succeeded in using (for the first time) a group of seismic guided Reservoir characteristic attributes to identify a sweet spot (in our case a sweet level) and develop a well path plan that properly placed the well trajectory deep enough away from Karst and surface irregularity avoiding the danger of drilling hazard and penetrating the upper layer, yet shallow enough not to fall within the high water zone. Our success story is supported by wire line image log and acoustic logs and keeping the well cost at reasonably estimated values. The paper reviews difficulty normally faced in drilling those wells, summarize original work carried out in selecting seismic guided attributes (as part of the reservoir characterization) to build a well design based on those findings. The paper will finally present a successful drilling case of 2700 ft supported by wireline logs. Introduction: The field is located in offshore Abu Dhabi and has been producing for the last 25 years. It covers about 400 Km2. The producing reservoir consists of series of middle cretaceous culminations covering almost the entire field. It is a reefal and shoal limestone build-up characterized by a typical thick bottom water drive reservoir. The dry oil interval is relatively thin and sited above huge transition zone. Since the field discovery, over 60 vertical, deviated and horizontal wells were drilled in the field as oil producers and water injectors. Many other wells were side tracked to be horizontalized for maximum reservoir exposure.

Abstract "Spectral Fusion" is a new tool designed to combine different seismic datasets covering the same surveyed area, with different but overlapping bandwidths resulting for instance from various source/receiver depths for marine streamer data. By combining data with overlapping spectra a larger bandwidth can be recovered, with the benefit of using all of the available information in the overlapping frequency domain without requiring any wave shaping or predefined filters, and therefore better preserving the individual phase and amplitude character of the input data. Driven by a real data application featuring a conventional streamer survey and a high-resolution field-oriented survey, it was developed as an alternate solution to a more complex inversion workflow. On the real data application presented, Spectral Fusion is shown to directly enable the bridging of the low-frequency gap in the high-resolution survey using the lower frequency data, allowing favorable comparison with a more complex inversion-based technique. It is also shown to bring an improvement in the quality of the resulting images as well as in the seismic well tie. Introduction The motivation for an amplitude-preserving non-destructive combination of partially overlapping low and high frequency datasets arose from a practical case of a specific field. The area was first covered by an exploration 3D survey with conventional acquisition parameters, and then several years later, though still before production started, by a dedicated survey whose parameters were tuned towards significantly higher frequencies. This last survey resulted in spectacular high-resolution reflectivity, but also had a lack of energy below 20 Hz. This lack of low frequencies became more obvious when the high frequency dataset was inverted for impedances. One can think of seismic inversion as a method to move from seismic reflectivities to absolute impedance. It requires bringing in the low frequencies missing in the seismic data using a background model, and finding an appropriate way to combine the relative seismic impedances with those from the background model. In this case, as is usual, the upper limit of the low frequencies brought in by the initial model is hardly more than a few Hertz, and the resulting low-frequency gap generated unacceptable secondary lobes in the inversion results. To circumvent this issue, the first idea was to solve the problem at the inversion level. Pillet et al. (2007) presented a specific application of pre-stack inversion aimed at using the low-frequency dataset in a first inversion in order to bridge the frequency gap in a second target inversion of the high-frequency seismic. In parallel to this workflow, which was carried forward to be used in operations, the geophysical research team at Total's Geosciences Research Center in Aberdeen took a different route and restated the problem directly from the seismic side. The method proposed does not use complementary low-pass and high-pass filters, which would be a simple way to recombine both datasets (Carter et al, 2009), but would not be appropriate for several reasons. The main drawback of this category of methods is the canceling of the large amount of redundant information in the common bandwidth whilst another is that they typically rely on pre-determined crossover filters and are therefore not self-adaptative.

Abstract Bul Hanine field is located offshore Qatar with primary oil production from the Reservoir-X carbonates. In 2005 and 2006, Qatar Petroleum recognized that future development of this mature field would require a modern, state of the art, reservoir model, and initiated several projects to achieve that goal: reprocessing and elastic inversion of the 1995 vintage 3D seismic, petrophysical data collection and analysis, and comprehensive reservoir characterization. This paper illustrates how Qatar Petroleum, with contractual assistance from PGS, Total and Beicip-Franlab, has applied advanced reservoir characterization techniques to constrain petrophysical property distribution using elastic inversion products and therein reducing uncertainty in a reservoir model. Following detailed rock typing core and log analysis from approximately 5400 feet of core and from 26 wells, and logs from 90 well penetrations, the team observed that there was considerable heterogeneity in this "hard" well data, and that distribution of the petrophysical properties between wells would suffer in the absence of additional control. To address the lack of inter-well control, an attempt was made to extract reservoir property information from the seismic data. Using optimally reprocessed existing 3D seismic data (to eliminate noise and preserve relative amplitudes) and pre-stack elastic inversion, advanced reservoir characterization techniques yielded volume data including lithology, lithology probability, and porosity that could be used as geo-statistical constraints. Initially, a detailed petro-elastic analysis was performed on select wells to calibrate well-derived elastic properties with seismic data in order to design the most appropriated seismic characterization workflow. The results demonstrated that acoustic and elastic impedances could be used to discriminate Calcites, Dolomites, and Anhydrites. Well analysis also indicated a robust relationship between impedance and porosity and each dominant lithology. Subsequently, a pre-stack inversion was conducted prior to 3D discriminate analysis to produce dominant lithology and associated probability volumes. Following this, seismic reservoir characterization resulted in generation of a lithology based porosity volume. During geo-statistical modeling, dominant lithology probability volumes were used as a co-simulation parameter for generating a lithology model, and the seismic porosity volume was used as co-simulation parameter for porosity distribution, resulting in a high-resolution static model of the Reservoir-X reservoir. This work demonstrates the added value of pre-stack seismic reservoir characterization for modeling of the Reservoir-X carbonate reservoir. In addition to porosity, this technique gives light to lithology changes throughout the reservoir, providing otherwise unobtainable information of rock-type distribution. Introduction Reservoir characterization is typically ground-truthed by well data; however wells are often a great distance from each other and thus provide only limited understanding of the actual lateral heterogeneity of a reservoir. Despite poor vertical resolution, seismic data is among the very few data types that provide lateral resolution in excess of that offer by wells alone. This paper presents a case study detailing the methodology for defining the relationship between seismic data and well petrophysical properties (lithology and porosity) and the subsequent generation of seismic derived constraints for high resolution geological modeling. The target of the reservoir characterization work is the nominally 200 foot thick Reservoir-X formation, a predominately carbonate interval that also contains dolomite and anhydrite layers. The project objectives were to generate seismic volumes that could provide quantitative value in reservoir characterization. Knowledge of the Reservoir-X lithology plays a critical part in identifying reservoir dynamic properties; often differing lithologies have similar porosity but dramatically different permeability. Defining dominant lithology from seismic data would offer significant improvement over a purely well log derived lithology trend. In intervals with homogeneous lithology the ability to define porosity trends enhances well log derived porosity volumes.