Seismic Reservoir Characterization Research Articles

Fault transformer: An automatic fault detection algorithm on seismic images using a transformer enhanced neural network

Seismic fault detection is a key step in seismic interpretation and reservoir characterization that often requires a large amount of human labor and interpretation time. Therefore, automatic seismic fault detection is critical for improving the efficiency of seismic data processing and interpretation. Existing artificial intelligence methods are mostly based on convolutional neural networks with a U-shaped encoder-decoder structure, known as U-net. However, the convolution is limited in modeling long-range correlative features. Instead, transformers, using self-attention mechanisms, avoid the local nature of the convolution, which has the potential to extract long-distance correlations. Transformers are proven to perform well in natural language processing, image classification, and segmentation tasks in precision and recall. Here, we develop a new deep neural network with transformers and a U-net-like structure: a fault transformer to perform the fault detection task. The new network outperforms the traditional U-net in the application with synthetic data sets.

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

3D rock physics template-based probabilistic estimation of tight sandstone reservoir properties

Quantitative prediction of reservoir properties (e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity to analyze these factors’ influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties’ impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.

Deep carbonate reservoir characterization using multiseismic attributes: A comparison of unsupervised machine-learning approaches

Seismic reservoir characterization is of great interest for sweet spot identification, reservoir quality assessment, and geologic model building. The sparsity of the labeled samples often limits the application of supervised machine learning (ML) for seismic reservoir characterization. Unsupervised learning methods, in contrast, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised learning approaches, including the linear method of principal component analysis (PCA), the manifold learning methods of t-distributed stochastic neighbor embedding and uniform manifold approximation and projection (UMAP), and the convolutional autoencoder (CAE), on the 3D synthetic and field seismic data of a deep carbonate reservoir in southwest China. On the synthetic data, the low-dimensional features extracted by UMAP and CAE provide a better indication of porosity and gas saturation than traditional seismic attributes. In particular, UMAP better preserves the global structure of geologic features and indicates the potential of decoupling the gas saturation and porosity effects from seismic responses. We demonstrate that joint use of several types of seismic attributes, instead of using a single type of seismic attributes, can better delineate the reservoir structures using unsupervised ML. On the field seismic data, UMAP can effectively characterize the sedimentary facies distribution, which is consistent with the geologic understanding. Nevertheless, the porosity and saturation can not be reliably identified from field seismic data using unsupervised ML, which is likely caused by the complex pore structures in carbonates complicating the mapping relationship between seismic responses and reservoir parameters.

Fast sparse Bayesian learning-based seismic resolution enhancement

Seismic high resolution processing plays a crucial role in seismic interpretation and reservoir characterization. However, the low and high frequency of seismic signal are lost owing to the effects of earth filtering and diffraction. The missing of low-frequency increases the energy of wavelet side lobe, which not only enhances the interference with adjacent events, but also lead to side lobe artifacts. High frequency attenuation is more serious, which significantly reduces the seismic resolution. In addition, the current resolution enhancement methods are difficult to achieve desirable result in noise contaminated environment. A novel resolution enhancement method that extends high and low frequencies simultaneously and improves the signal-to-noise ratio (SNR) under the framework of sparse decomposition theory is proposed. Firstly, an over-complete dictionary is constructed using non-zero phase Ricker wavelet and then the effective seismic signal is extracted by sparse decomposition using the fast sparse Bayesian learning (FSBL) algorithm. Secondly, the effective frequency range of the seismic signal is determined through a series of atoms obtained by sparse decomposition. Then, a desired broadband spectrum can be identified depending on this range. By approximating the broadband spectrum of seismic data, the weak high-frequency information can be extended. Furthermore, a correction term is introduced in frequency domain to suppress the side lobes of atoms. The side lobes are well suppressed without distorting the main lobe, meanwhile, both the low and high frequencies are extended. Finally, the validity of this method is verified by synthetic and field data. The results show that this method has better performance than the ordinary deconvolution and spectral whitening methods in processing high resolution data.

Lithology and Porosity Distribution of High-Porosity Sandstone Reservoir in North Adriatic Using Machine Learning Synthetic Well Catalogue

Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho–fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role.

Gas channel delineation utilizing a neural network and 3D seismic attributes: Simian Field, offshore Nile Delta, Egypt

Depositional architecture and hydrocarbon-bearing reservoir detection are considered the main challenges in the Nile Delta Basin, predominantly in the slope area. Channel fill deposits are very complicated because they are vertically stacked and pass through different depositional stages, forming complex depositional patterns. In the current study, an artificial neural network was used to detect gas and water-bearing reservoirs inside the complex depositional pattern and their spatial dimensions. Modern seismic reservoir characterization attribute analysis provides a good outcome in detecting reservoir fluids by mixing fluid-sensitive attributes with the well outcomes. Artificial Neural Network (ANN) algorithms for rock property prediction are considered to be a new emerging trend. The ANN algorithm has an advantage over other seismic reservoir delineation techniques because it has a good capability for building a nonlinear relationship between seismic data and petrophysical logs.Hence, it can be utilized to spatially anticipate reservoir fluids with a rational degree of accuracy. A back-error propagation algorithm, a supervised neural network method, was implemented on the Simian Field to anticipate the fluid-bearing reservoirs from the seismic-derived amplitudes. The ANN method showed unique results in the case study area by discriminating the gas-bearing intervals from the water-bearing intervals, although combining the assembly of the seismic attributes to supervise fluid classification. The method will help in the development plans of the field and track the volume of water and gas.

Seismic reservoir characterization of the syn-rift lower Miocene Rudeis Formation in the July oilfield, Gulf of Suez basin, Egypt: Implication for reservoir quality assessment

The Lower Miocene Rudeis Formation is among the most potential oil-bearing sedimentary sequences in the Gulf of Suez (GOS), representing the main producer of Egyptian oil. This study concerns primarily with revealing the reservoir characteristics and facies distribution, as well as the implication of the predominant diagenetic factors on the quality of the Lower Miocene syn-rift Rudeis reservoir in the July oilfield. Therefore, this study integrates 2D seismic, and wireline logs, as well as conventional and special core analyses to determine the reservoir characteristics and facies distribution. The interpreted data elucidate four different seismic facies in the Rudeis Formation with a net pay thickness ranging from 29 to 233 m in the studied wells of 16.3% average porosity, 62.9% average oil saturation, and 9.3% average shale volume. Four seismic facies (SF1-SF4) were interpreted and correlated with the Rudeis clastic deep-water turbiditic succession. The 3D static property models revealed that the best reservoir facies in terms of low shale content and water saturation, as well as high porosity, coincide with the submarine channels and fans containing coarse-grained, clean sandstones. From the core analysis, the Lower part of the Rudeis sequence is subdivided into five reservoir rock types (RRTs). The RRT1 is characterized by the best reservoir quality; it is composed of quartz arenites, sublithic arenites, and subfeldspathic arenites. In contrast, the RRT5 represents the lowest reservoir quality; it consists of very coarse siltstone, laminated glauconitic siltstone, and highly compacted dolomitic and calcareous quartz arenites. The poor reservoir characteristics of the RRT5 are attributed to tight compaction, cementation, and early dolomitization phase, whereas the highest quality of the RRT1 is related to fracturing and dissolution processes. The RRT1 rocks are mostly associated with the submarine channel and fan facies, whereas the poor quality RRT5 sediments were accumulated as hemipelagic/background. This integrated workflow study can be applied to further case studies in the Middle East, Africa, and the other analogues all over the world.

Seismic interpolation via multi-scale HU-Net

The recovery of the seismic data with missing traces is essential for seismic interpretation and reservoir characterization. The deep learning (DL) based models have shown the promising results for the challenging task of seismic data interpolation. However, interpolating the large gap missing traces is a big challenge for seismic interpolation. The holistically-nested edge detection (HED) model can learn the multi-scale and multi-level features for the holistic image training and prediction. The U-Net model has a large number of feature channels in the upsampling part, which allow it propagate the feature maps to the higher resolution layers. Utilizing the advantages of these two state-of-art DL models, we propose a novel convolution neural network (CNN) based model for seismic interpolation, named the HU-Net model, by integrating the holistically-nested module and the U-net model. To formalize this, we add five activation layers followed by the U-Net model to replace the origin sigmoid layer. We then use five side branches to connect the decoder with these activation layers and output multi-scale features. Thus, our proposed HU-Net cannot only recognize the objects in big images, but also reconstruct the multi-scale features. We investigate the ability of our proposed HU-Net for interpolating the missing seismic traces with big gaps and reconstruct the fining sequence texture simultaneously. The synthetic and field data examples illustrate the effectiveness and robustness of our proposed HU-Net model. The detailed comparisons also demonstrate the superiority of our proposed model over the conventional methods.

High-Resolution Seismic Characterization of Gas Hydrate Reservoir Using Wave-Equation-Based Inversion

The high-resolution seismic characterization of gas hydrate reservoirs plays an important role in the detection and exploration of gas hydrate. The conventional AVO (amplitude variation with offset) method is based on a linearized Zoeppritz equation and utilizes only the reflected wave for inversion. This reduces the accuracy and resolution of the inversion properties and results in incorrect reservoir interpretation. We have studied a high-resolution wave-equation-based inversion method for gas hydrate reservoirs. The inversion depends on the scattering integral wave equation that describes a nonlinear relationship between the seismic wavefield and the elastic properties of the subsurface medium. In addition to the reflected wave, it considers more wavefields including the multiple scattering and transmission during inversion to improve the subsurface illumination, so as to enhance the accuracy and resolution of the inversion properties. The results of synthetic data from Pearl River Mouth Basin, South China Sea, demonstrate the validity and advantages of the wave-equation-based inversion method. It can effectively improve the resolution of inversion results compared to the conventional AVO method. In addition, it has good performance in the presence of noise, which makes it a promising method for field data.

Seismic reservoir characterization of the Gassum Formation in the Stenlille aquifer gas storage, Denmark — Part 2: Unsupervised classification

Ideally, a good static reservoir model provides an accurate estimate of the extent, porosity, permeability, and lithology of the container as well as the properties of the seal and any faults or fractures that may allow the reservoir to leak. The major pitfall of deterministic, statistical, or supervised learning workflows is that they estimate only the properties sampled by the wells or provided by empirical relations and may miss mapping heterogeneities in the reservoir and seal that can give rise to flow baffles and reservoir leakage. This shortcoming is exacerbated when the number of wells is small, the types of logs recorded are limited, and the migrated seismic gathers are absent or of limited quality. In contrast, unsupervised learning looks for patterns in the seismic amplitude and attribute volumes themselves. In this paper, we apply unsupervised learning algorithms to evaluate the natural gas storage Stenlille aquifer in Denmark and compare the results with a supervised multiattribute regression reservoir characterization described in a companion paper. Specifically, we apply principal component analysis, self-organizing mapping, and generative topographic mapping workflows to extract patterns across eight attribute volumes: relative acoustic impedance, energy, sweetness, gray level co-occurrence matrices (GLCM) entropy, curvedness, and three spectral magnitude volumes. We find that the large-scale patterns are similar, but that the unsupervised learning algorithms provide greater detail. Because our deterministic model was built on poststack data using the limited well-log data available, we believe that the heterogeneity mapped by the unsupervised learning workflows provides a relatively unbiased means of estimating risk in our reservoir model. Quantifying the importance of these anomalies will need to be reconciled with a dynamic reservoir model.

Seismic reservoir characterization of the Gassum Formation in the Stenlille aquifer gas storage, Denmark — Part 1

Seismic reservoir characterization plays an important role in carbon capture and storage analysis. The Havnsø anticlinal structure in Denmark is a prospective CO2 storage site due to its proximity to two large emission sources—a coal-fired power station and a nearby refinery. Although legacy 2D seismic lines over the area outline the anticlinal structure, their quality is insufficient for quantitative interpretation. Earlier studies have shown that the natural gas stored in the Stenlille aquifer exhibits a seismic response similar to the modeled CO2 fluid in the Havnsø structure. Thus, seismic reservoir characterization carried out on the Stenlille aquifer gas storage in terms of identifying spatial distribution of gas and outlining faults would provide insight regarding value addition that seismic data can bring into the proposed CO2 storage at Havnsø. Using the available poststack seismic data, we apply an integrated reservoir characterization analysis. After performing the adequate data conditioning, the impedance of the target Stenlille Formation is estimated through generation of an accurate low-frequency model. Thereafter, multiattribute analysis was used to generate volumetric estimates of porosity, gamma ray, and water saturation within the target formation so that the spatial distribution of gas can be mapped. The resulting porosity and gamma-ray volumes indicate encouraging results and were used for Bayesian classification to predict the probability of the more important lithofacies, namely, sand, shale, moderate-porosity sand, and moderate-porosity shaly sand, which enabled the mapping of high-porosity/facies zones in the two aquifer storage levels. Independently, we make use of unsupervised machine learning applications for seismic facies prediction and compare them at the two storage levels, which will be presented in the part 2 of this paper.

Editorial for the Special Issue: “Studies of Seismic Reservoir Characterization”

Seismic reservoir characterization plays an essential role in the study of integrated reservoirs, with applications from prospect identification to detailed reservoir delineation [...]

Anisotropic three-term amplitude-variation-with-offset projections: A case study

A seismic amplitude-variation-with-offset (AVO) analysis is an important element in seismic reservoir characterization and has been successfully applied in many fields. Traditionally, AVO studies are based on AVO intercept ([Formula: see text]) and gradient ([Formula: see text]) only; curvature ([Formula: see text]) is neglected because it is very sensitive to noise and avoiding noisy [Formula: see text] is very difficult in practice. The recently introduced concept of three-term (3T) AVO projections, combined with continuous improvements in seismic data acquisition and processing, allows interpreters to effectively use [Formula: see text] by optimizing projection angles based not only on rock physics but also on seismic noise. The effectiveness of the 3T AVO projection, in which [Formula: see text], [Formula: see text], and [Formula: see text] are used as the triplet of reflectivities that are summed in the projection, and the impact of random noise on the 3T AVO projection have been investigated using data from an offshore Western Australian field. The 3T AVO projection is used in the framework of the extended elastic impedance. Because the study area is known to have nonnegligible differences in anisotropy parameters between the various lithologies, its impact on [Formula: see text] and [Formula: see text] is taken into account. First, the 3T projection angle targeting lithology fraction logs is optimized using well-log data. Analyses using synthetic seismograms are then performed to investigate the impact of seismic noise. Finally, the method is applied to field data. The results find that (1) [Formula: see text] is an important component of seismic reservoir characterization even if it appears unusable and (2) seismic noise must be taken into account when optimizing the projection angle.

Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples

The physics that describes the seismic response of an interval of saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics, and wave propagation models. The main goal of seismic reservoir characterization is to predict the rock and fluid properties given a set of seismic measurements by combining geophysical models and mathematical methods. This modeling challenge is generally formulated as an inverse problem. The most common geophysical inverse problem is the seismic (or elastic) inversion, i.e., the estimation of elastic properties, such as seismic velocities or impedances, from seismic amplitudes and traveltimes. The estimation of petrophysical properties, such as porosity, lithology, and fluid saturations, also can be formulated as an inverse problem and is generally referred to as rock-physics (or petrophysical) inversion. Several deterministic and probabilistic methods can be applied to solve seismic inversion problems. Deterministic algorithms predict a single solution, which is a “best” estimate or the most likely value of the model variables of interest. In probabilistic algorithms, on the other hand, the solution is the probability distribution of the model variables of interest, which can be expressed as a conditional probability density function or a set of model realizations conditioned on the data. The probabilistic approach provides a quantification of the uncertainty of the solution in addition to the most likely model. Our goal is to define the terminology, present an overview of probabilistic seismic and rock-physics inversion methods for the estimation of petrophysical properties, demonstrate the fundamental concepts with illustrative examples, and discuss the recent research developments.

Seismic reservoir characterization and pressure depletion of the Abu Madi Formation in the NW Khilala field, onshore Nile Delta, Egypt

The Late Messinian Abu Madi Formation represents the most prospective deep reservoir target in the Nile Delta. Hydrocarbon exploration in the Nile Delta over the last few decades has been targeted to understand the Abu Madi reservoir quality and distribution. The integration of rock physics, amplitude versus angle (AVA), seismic interpretation, seismic attributes, and seismic inversion could successfully determine the characteristics of the late Messinian Abu Madi Formation in the North West Khilala (NWKh) field, which is located in the central onshore part of the Nile Delta, Egypt. The NWKh field is located on the eastern margin of the Disouq concession close to producing the West Khilala (WKh) field, with highly heterogeneous reservoir parameters. The reservoir is classified as lacustrine turbidites deposited in semi-isolated Late Messinian Basins. The Abu Madi Formation can be subdivided into upper and lower sand members. It is very common that they are separated by an intraformational shale layer. AVA analysis performed on the top of the Abu Madi reservoir suggested a class 2p. Seismic data were inverted to delineate the overall lateral extent of the reservoir. The prestack inversion process was integrated with rock physics modelling of the wells to generate high-resolution multiple rock property models to characterize the reservoir and observe or investigate different pressure depletion trends. The pressure depletion of the lower Abu Madi in the North West Khilala field was investigated laterally and vertically by the relative/full spectrum inversion and formation modular dynamic tester (MDT) pressure data, respectively. This depletion is due to communication with the West Khilala field, which was produced from the Lower Abu Madi (LAM) sand interval in 2007.

Prediction of porosity and water saturation using a probabilistic neural network for the Bahariya Formation, Nader Field, north western desert, Egypt

Predicting petrophysical parameters, particularly saturation, is a common challenge due to the lack of direct relationships with seismic elastic attributes. Therefore, in this paper, we used one of the Artificial Intelligence (AI) algorithms for seismic reservoir characterization to overcome this challenge. A new trend is the use of Artificial Neural Network (ANN) algorithms to predict reservoir characteristics. The capacity to construct nonlinear relationships between petrophysical logs and seismic data is the fundamental advantage of the ANN algorithm. A probabilistic neural network (PNN) was the selected algorithm. The input data includes seismic full-stack and log data for four wells. Multi-attribute linear regression and PNN were applied to the Nader field north western desert to predict water saturation (Sw) and effective porosity (PHIE) for the Bahariya reservoir. Stepwise regression, which derives subsets of attributes from internal and external attributes, is used in a multi-attribute analysis. External attributes are represented in acoustic impedance generated from seismic inversion, whereas internal attributes are represented in well logs and seismic data. The seismic inversion technique has several types, but model-based inversion is the focus of this study. The training of a neural network (NN) using three attributes in Sw prediction and using six attributes in PHIE prediction showed a substantial number of correlations. The actual and predicted water saturations and porosities of the PNN have correlation coefficients of 97.5% and 91%, respectively. Then, Sw and PHIE parameters were extended over the Bahariya reservoir to describe the distribution of these parameters in Nader field. The results prove the validity of the workflow to accurately predict Sw and PHIE with a higher accuracy than ever before in the north western desert. Hence, this workflow can be implemented in the analogue basins in the future.

Consistency and prior falsification of training data in seismic deep learning: Application to offshore deltaic reservoir characterization

Deep learning (DL) applications of seismic reservoir characterization often require the generation of synthetic data to augment available sparse labeled data. An approach for generating synthetic training data consists of specifying probability distributions modeling prior geologic uncertainty on reservoir properties and forward modeling the seismic data. A prior falsification approach is critical to establish the consistency of the synthetic training data distribution with real seismic data. With the help of a real case study of facies classification with convolutional neural networks (CNNs) from an offshore deltaic reservoir, we have highlighted several practical nuances associated with training DL models on synthetic seismic data. We highlight the issue of overfitting of CNNs to the synthetic training data distribution and propose regularization strategies to address it. We demonstrate the efficacy of our proposed strategies by training the CNN on synthetic data and making robust predictions with real 3D partial stack seismic data.

Self-adaptive denoising net: Self-supervised learning for seismic migration artifacts and random noise attenuation

Seismic noise attenuation is essential for seismic interpretation and reservoir characterization. Recently, many researchers have applied convolutional neural network (CNN) to attenuate seismic noise. We propose a novel self-supervised CNN based approach to attenuate seismic random noise and migration artifacts simultaneously, termed as self-adaptive denoising net (SaDN). In this approach, we modify the loss-function of denoising CNN (DnCNN) based on the assumption that the synthetic noise with mixed Gaussian-Poisson distribution can simulate random noise and migration artifact. Moreover, we develop a new framework for the training and testing procedure. By adding the synthetic noise to seismic data, we can use the proposed CNN model to learn the characteristic of signal-dependent noise. The additive signal-dependent noise is self-adapted to the original seismic data according to the statistical property. Then, the well-trained SaDN model is applied to the original 3D seismic survey to recognize and extract the original random noise and migration artifact. The SaDN is training in the original seismic data with synthetic noise and testing in the same targeting seismic data without synthetic noise. Besides, our method does not require other denoising method for preprocessing in the training data. Thus, our proposed SaDN can be trained in a self-supervised way and adapted to different field data. Different experiments have been tested and compare with the method of CWT to illustrate the robustness and superiority of our proposed SaDN. The experiment results indicate that our proposed SaDN is able to remove the seismic random noise and migration artifacts without harming the effective signal.

A shared earth approach to siliciclastic reservoir characterization: The Zubair Fm of Northern Kuwait

An innovative, integrated characterization workflow is applied to complex siliciclastic reservoirs of the Lower Cretaceous Zubair Formation in the Bahrah Field of Northern Kuwait. A detailed sedimentological and petrophysical assessment, integrated with 3D seismic interpretation, constrained and delineated vertical and lateral facies changes within the field, which is also affected by complex fault and fracture patterns. Siliciclastic geo-bodies have been documented and mapped in 1D, 2D and 3D using seismic stratigraphy, seismic attributes and high-resolution seismic inversion reservoir characterization workflows. The spatial distribution of these geo-bodies appears to show the development of fluvio-deltaic systems in the southern and central sectors of the field, which evolved into marine dominated/tidally influenced deltaic systems towards the northern and eastern sectors. Two major fluvial channels trends have been identified: SW-NE and W-E, with a dominantly NE trending deltaic progradation. Towards the north, elongated (tidal mouth?) sand bars have been interpreted with a NW-SE trend. An integrated sequence stratigraphic framework was consequently developed to better assess the influence of relative sea level changes on sedimentary facies. This framework pointed to the influence of marine incursions and subsequent flooding surfaces, which lead to the development of shale-rich seals and/or baffles that could increase the probability of structural-stratigraphic trapping mechanisms in these depositional environments. This integrated workflow was utilized to update a numerical static reservoir model, which further de-risked future drilling locations and potentially charged reservoir volumes.