3D Reservoir Research Articles

Implicit Tetrahedral and Surface Mesh Generation for 3D Reservoir Structural Modeling

Implicit Tetrahedral and Surface Mesh Generation for 3D Reservoir Structural Modeling

Sensitivity and Uncertainty Analysis for Natural Gas Hydrate Production Tests in Alaska

The Japan Research and Development Consortium for Pore Filling Hydrate in Sand (MH21-S), the U.S. Department of Energy National Energy Technology Laboratory (DOE NETL), and the U.S. Geological Survey (USGS) are planning a long-term production test from gas hydrate (methane hydrate) bearing reservoirs in the Prudhoe Bay Unit in northern Alaska. Prediction of gas and water production using 2D reservoir simulation models can be very useful for decision-making for long-term production testing. However, the input parameters applied to the simulation model, which are essentially based on the most likely values obtained from the well-log interpretation and core sample measurements of the Hydrate-01 stratigraphic test well (STW), have some degrees of uncertainty. In this study, to evaluate the impact of uncertainties in the input model on the prediction results of gas and water production as well as to analyze how each input parameter contributes to the uncertainties of prediction results, we conducted uncertainty analysis and parameter sensitivity analysis based on two different methods (the regression-based method and the Sobol’ method). The sensitivity analyses reveal that the parameters regarding relative permeability (exponential of the gas relative permeability (Ng) and residual gas saturation (Sgcr)) and the intrinsic/effective permeability of the lower B1 sand significantly contribute to the uncertainties of the gas and water production predictions. In addition, the uncertainty analyses reveal that the P50 value of cumulative gas production can result in less than the reference case with the most likely values for the input parameters due to the uncertainty of Ng. These results indicate that reducing the uncertainties of gas relative permeability and residual gas saturation is essential for more reliable numerical simulations both for prediction of test well performance and subsequent history matching of field test results.

GANSim‐3D for Conditional Geomodeling: Theory and Field Application

AbstractWe present a Generative Adversarial Network (GAN)‐based 3D reservoir simulation framework, GANSim‐3D, where the generator is progressively trained to capture geological patterns and relationships between various input conditioning data and output earth models, and is thus able to directly produce multiple 3D realistic and conditional earth models from given conditioning data. Conditioning data can include 3D sparse well facies data, probability maps, and global features, such as facies proportion. The generator only includes 3D convolutional layers, and once trained on a data set consisting of small‐size data cubes, it can be used for geomodeling of 3D reservoirs of large arbitrary sizes by simply extending the inputs. To illustrate how GANSim‐3D is practically used and to verify GANSim‐3D, a field karst cave reservoir in Tahe area of China is used as an example. The 3D well facies data and 3D probability map of caves obtained from geophysical interpretation are taken as conditioning data. First, we create training, validation, and test datasets consisting of 64 × 64 × 64‐size 3D cave facies models integrating field geological patterns, 3D well facies data, and 3D probability maps. Then, the 3D generator is trained and evaluated with various metrics. Next, we apply the pretrained generator for conditional geomodeling of two field cave reservoirs of size 64 × 64 × 64 and 336 × 256 × 96, respectively. The produced reservoir realizations prove to be diverse, consistent with field geological patterns and field conditioning data, and robust to noise in the 3D probability maps. Each realization with 336 × 256 × 96 cells only takes 0.988 s using 1 GPU.

3D facies and reservoir property prediction of deepwater turbidite sands; case study of an offshore Niger delta field

The distribution of lithofacies and key reservoir properties such as porosity, net to gross and water saturation is essential to describing reservoir heterogeneity predicted within a clastic deep water reservoir unit in the study area. A systematic analysis of core and gamma-ray wireline logs was used to identify the reservoir unit in this study. The identified reservoir unit was mapped on 3D seismic data, and four lithological and fluid sensitive seismic attributes extracted for the reservoir were fed as inputs into an unsupervised artificial neural network (UANN) analysis to define the depositional pattern of the reservoir unit. Afterwards, a 3D sand facies trend model was built using the facies depositional pattern map and upscaled properties from well data. The generated 3D sand probability model from trend modelling was subsequently used to constrain geostatistical facies modelling of the reservoir unit. The systematic stratal analysis from core, well data and the integration of seismic attributes using UANN reveal the reservoir unit as a system of turbidite lobe sands and turbidite channel levees sands. The 3D Geostatistical facies distribution reveals spatial and vertical facies heterogeneity. It indicates high sand amalgamations and static hydraulic connectivity of turbidite lobe sands in the distal part of the study area. Further geostatistical distribution of reservoir properties conditioned to facies indicated good to excellent reservoir quality within the turbidite lobe sands, moderate to low reservoir quality within turbidite channel sands and poor reservoir quality at the turbidite channel margins and the deep-water shale facies. The 3D facies and reservoir property models built in this study would ensure optimisation of the reservoir unit, enable better estimation of hydrocarbon volumes and serve as input data for flow simulation.

Uncertainty propagation and sensitivity analysis of three-phase flow in porous media using polynomial chaos expansion

In this study, polynomial chaos expansion (PCE) will be utilized to investigate propagation and evolution of uncertainty from uncertain model parameters to model outputs. Unknown PCE coefficients are calculated using probabilistic collocation method (PCM), which is a non-intrusive technique. The proposed approach is evaluated on a 2D reservoir under three-phase flow condition. By use of PCEs, dependent model outputs, such as well bottomhole pressure and production rates, are represented as functions of uncertain model parameters, including parameters defining the capillary pressure (Pc) and relative permeability (kr) curves. Accuracy, compatibility and efficiency of the proposed approach are compared with Monte Carlo (MC) method, which is considered as the reference case. Mean values, standard deviations and histograms for well bottomhole pressure and production rates estimated from the PCM-based PCE are closely matched to those obtained from MC method. This discloses the fact that the proposed approach can successfully restore the simulation model to represent propagation of uncertainty from uncertain model parameters toward model outputs and to perform sensitivity analysis (SA). Unlike the MC approach which requires an extremely large number of realizations to obtain accurate results, the PCM-based PCE requires to perform model evaluations only at the selected collocation points, thus the computational cost is significantly reduced. Subsequently, using the proposed PCE as the surrogate model of the reservoir, Sobol's sensitivity indices are calculated analytically. Results of SA reveal that those parameters defining Pc curves have negligible effect on the model outputs compared with those parameters defining kr curves, and thus may be ignored during history matching process. Moreover, it is observed that the effects caused by interactions between uncertain kr and Pc parameters have negligible contribution compared to the individual effect of each parameter.

3D Reservoir Modeling of the Farewell Formation and its Implications on the Maui Gas Field Development, Taranaki Basin, New Zealand

3D Reservoir Modeling of the Farewell Formation and its Implications on the Maui Gas Field Development, Taranaki Basin, New Zealand

A vector-to-sequence based multilayer recurrent network surrogate model for history matching of large-scale reservoir

History matching can estimate the parameter of spatially varying geological properties and provide reliable numerical models for reservoir development and management. However, in practice, high-dimension, multiple-solutions and computational cost are key issues that restrict the application of history matching methods. Recently, the combination of deep-learning-based surrogate model and sampling algorithm has been widely studied in history matching to overcome the limitations. Considering that real-world large-scale reservoirs often have hundreds of thousands or even millions of grid-based uncertain parameters, extracting spatial features using convolutional neural networks requires a lot of computational cost and storage requirements. Therefore, in this work, we mainly study how to use the recurrent neural network (RNN) to construct the surrogate model for history matching. Specifically, we propose a multilayer RNN surrogate model based on a vector-to-sequence modeling framework. The multilayer RNN surrogate model with gated recurrent unit (GRU), termed MLGRU, is developed to approximate the mapping from feature vector of geological realizations to the production data. The feature vector is the low-dimensional representation of geological parameter fields after using the re-parameterization method, while production data are the simulation results of historical period. In addition, we design a log-transformation-based windowed normalization (LTWN) method for the production data, which can enhance the learnability and features of production data. The MLGRU model is incorporated into a multimodal estimation of distribution algorithm (MEDA) to formulate a history matching workflow. The hyperparameters and performance of the proposed MLGRU model are analyzed by numerical experiments on a 2D reservoir model. Furthermore, numerical experiments performed on the Brugge benchmark model, a large-scale 3D reservoir model, demonstrated the performance of the proposed surrogate model and history matching method.

A mathematical framework for multiphase poromechanics in multiple porosity media

Unconventional geomaterials often exhibit multi-modal pore size distribution. We have developed a comprehensive framework for porous media exhibiting multiple porosity scales that are saturated with one or two types of fluids using mixture theory. Both the governing equations and constitutive laws have been clearly derived and identified, respectively. The effective stress σ′ emerged from the energy balance equation is adoptable for both elastic and elastoplastic deformations, in which pore fractions and saturations play a central role. The proposed model is general in a sense that it works for both uncoupled simulation and coupled simulation. The field equations for uncoupled flow simulation are solved using the Laplace transform and numerical Laplace inversion methods. By visualizing the dimensionless results, we can gain a quantitative insight of the different stages in the depletion process of a naturally fractured reservoir. For coupled flow and geomechanics simulation, a strip load problem and a two-phase flow in a deformable 3D reservoir problem illustrate the impacts of plasticity, multiple porosity, inter-porosity exchange, and capillary pressure on the system response.

Introduction to this special section: The North Sea

The North Sea is a shallow, northeastern arm of the Atlantic Ocean. The region is bounded by the United Kingdom, Norway, Denmark, The Netherlands, Germany, Belgium, and France. The UK and Norwegian sectors, in particular, have experienced a long history of offshore oil and gas since production started in 1967. The UK and Norwegian sectors boast a high average oil recovery factor of 46% ( Shepherd, 2017 ). Key to the success of industry has been the consistent application of new technologies such as 3D seismic, horizontal drilling, 3D reservoir simulation, enhanced oil recovery, and 4D seismic, the lattermost of which, in particular, has experienced more than 30 years of application on the UK and Norwegian continental shelves. As companies embrace the energy transition and the United Kingdom and Norway set their net-zero targets toward 2050, carbon capture and storage (CCS) is growing as a key technology in promoting a low-carbon solution to boost the economic future for the region (alongside hydrogen, wind power, and tidal). Importantly, the recent North Sea Transition Deal ( OGUK, 2021 ) announced by the UK government aims to harness the technological power of the oil and gas sector and anchor it to the energy transition. This deal aims to drive continued growth in the innovation and creativity for which the region is renowned. Abandoned reservoirs will be repurposed, new saline aquifers are being found, and renewables will coexist alongside CCS solutions. We envisage that a key component of this technology growth is the use of geophysics, specifically seismic data.

Effect of permeability heterogeneity on the dissolution process during carbon dioxide sequestration in saline aquifers: two-and three-dimensional structures

The convection–diffusion process of carbon dioxide (CO2) dissolution in a saline reservoir is investigated to shed light on the effects of the permeability heterogeneity. Using sequential Gaussian simulation method, random permeability fields in two and three-dimension (2D and 3D) structures are generated. Quantitative (average amount of the dissolved CO2 and dissolution flux) and qualitative (pattern of the dissolved CO2 and velocity streamlines) measurements are used to investigate the results. A 3D structure shows a slightly higher dissolution flux than a 2D structure in the homogeneous condition. Results in the random permeability fields in 2D indicates an increase in the standard deviation of the permeability nodes enhances the dissolution efficiency, fluctuations in CO2 dissolution flux, separation between the different realizations from the same input parameters, and tendency toward more jagged convective fingers’ shape. Furthermore, the distance between the permeability nodes increases the convective fingers’ dissolution efficiency and jagged structure. The degree of freedom in 3D structures results in a higher chance of escaping from the low permeability zones and reduces the interactions between convective fingers in 3D systems. With the same variance and correlation length between permeability nodes, connectivity between high permeable zones in 3D cases are less than that of 2D cases; therefore, 2D realizations overestimate the dissolution flux of real heterogeneous 3D structures, which should be considered carefully.Article HighlightsCO2 sequestration in two and three dimensional heterogeneous saline aquifers are investigated.3D structures in homogeneous conditions show higher dissolution than 2D structures.2D realizations overestimates the dissolution flux over real heterogeneous 3D reservoirs.

A Flow Feature Clustering-Assisted Uncertainty Analysis Workflow for Optimal Well Rates in Waterflood Projects

Summary The optimal schedule based on single geologic model may not necessarily result in favorable outcomes on the real field due to geologic uncertainty. This paper proposes an efficient workflow to evaluate the uncertainty of optimal well rates in waterflood problems. Specifically, a flow feature clustering method is derived using streamline and unsupervised machine-learning techniques to minimize the number of geologic realizations needed for geologic uncertainty representation, thus significantly accelerating the workflow. Given a set of historical production and injection data, first, an ensemble of Nreal history-matched geologic realizations is generated using ensemble-smoother with multiple data assimilation (ESMDA). Subsequently, the streamline time of flight (TOF) and principal component analysis (PCA) are used to extract the flow feature of all realizations, based on which k-means clustering algorithm generates a subset of Nclust realizations representing the whole ensemble. The rate optimization is performed on each of the representative realizations using a streamline-based rate optimization algorithm that seeks to maximize the oil production during the optimization period. The distribution of optimal schedules obtained by optimizing the representative realizations is shown to be in high correspondence with that obtained by optimizing the full ensemble. Using the optimal schedule distribution, the key wells are identified, for which rate change is advised with high certainty. The workflow is tested on a synthetic 2D reservoir model as well as a 3D field-scale benchmark reservoir model [sensitivity analysis of the impact of geological uncertainties on production (SAIGUP) model]. The novelty of this work is the combination of the streamline-extracted flow features and unsupervised machine-learning methods to formulate an efficient workflow for uncertainty analysis of optimal well schedules. The proposed approach ensures quality and rigor of uncertainty analysis with a significantly reduced number of geologic realizations and thus makes the approach well-suited for large-scale field applications.

Robust Value Quantification of Enhanced Oil Recovery Methods Using Ensemble-Based Optimization

Summary Over the last decades, the development of different oil reservoirs with gas or chemical enhanced oil recovery (EOR) methods have been studied, and the EOR effects of the recovery methods are found to be sensitive to fluid and rock properties in the reservoirs. The use of a single reservoir model with the assumption of known reservoir parameters is not enough to guarantee an accurate prediction of EOR effects. The best decision related to the injection strategies of EOR methods can be appropriately found using an optimization setup that accounts for the uncertainty quantification in the reservoir. In this paper, we present mathematical tools for optimizing and ranking the value of the commonly used EOR methods. The methodology is demonstrated with Smartwater, carbon dioxide (CO2), and polymer EOR methods on synthetic 2D and 3D oil reservoirs. To capture the uncertainties in the reservoirs, we use an ensemble of geological realizations obtained by engineering upscaling of the initial model. The usefulness of this study is to improve the understanding of the actual benefit of EOR methods and to provide a methodology that quickly allows users to appropriately predict EOR injection strategies that maximize the annually discounted economic values of the injected and production data. The control variables of optimization problems include EOR gas rate or chemical concentration, water rates, oil rate, or bottomhole pressures. An ensemble-based optimization method with covariance adaptation is used to solve the optimization problem. For the different reservoirs considered, we find the optimal well controls for EOR methods. A comparative study of the economic benefits of the optimal solutions of EOR methods using optimized waterflooding as a reference point is presented. CO2 is found to rank high compared with other EOR methods in both cases. Finally, we investigate the effect of different injection costs of CO2 on the optimization results of CO2 EOR methods for the 3D reservoir field.

Effects of Hydrogeological Heterogeneity on CO2 Migration and Mineral Trapping: 3D Reactive Transport Modeling of Geological CO2 Storage in the Mt. Simon Sandstone, Indiana, USA

We used three-dimensional (3D), high-resolution simulations facilitated by parallel computation to assess the effect of hydrogeological heterogeneity in the Mt. Simon Sandstone on CO2 plume evolution and storage and geochemical reactions in a portion of the Illinois Basin, Indiana. Two scenarios were selected to investigate the effects of the hydrogeological heterogeneity in 3D reactive transport simulations: a heterogeneous case with variable porosity and permeability, and a homogenous case with constant porosity and permeability. The initial pressure, temperature, and mineralogical distributions are consistently applied in both the heterogeneous case and the homogeneous case. Results indicate that including hydrogeological heterogeneity in 3D reservoir simulations for geological CO2 storage significantly impacts modeling results for plume migration patterns, CO2-water-mineral interaction, reservoir quality, and CO2 plume containment. In particular, results indicate that (1) the CO2 plume reached the top of the Mt. Simon Sandstone in the homogeneous case, but was restrained to the lower third of the formation when hydrogeologic heterogeneity was considered; (2) the dominant trapping mechanism in the heterogeneous case was mineral trapping (43%), while it was solubility trapping (47%) in the homogeneous case (at 10,000 years); (3) incorporating reservoir heterogeneity in the model leads to a higher likelihood of long-term containment.

A novel hybrid recurrent convolutional network for surrogate modeling of history matching and uncertainty quantification

Automatic history matching (AHM) has been widely studied in petroleum engineering due to it can provide reliable numerical models for reservoir development and management. However, AHM is still a challenging problem because it usually involves running a great deal of time-consuming numerical simulations during the solving process. To address this issue, this article studies a hybrid recurrent convolutional network (HRCN) model for surrogate modeling of numerical simulation used in AHM. The HRCN model is end-to-end trainable for predicting the well production data of high-dimensional parameter fields. In HRCN, a convolutional neural network (CNN) is first developed to learn the high-level spatial feature representations of the input parameter fields. Following that, a recurrent neural network (RNN) is constructed with the purpose of modeling complex temporal dynamics and predicting the production data. In addition, given that the fluctuations of production data are influenced by well control measures, the well control parameters are used as auxiliary inputs of RNN. Moreover, the proposed surrogate model is incorporated into a multimodal estimation of distribution algorithm (MEDA) to formulate a novel surrogate-based AHM workflow. The numerical studies performed on a 2D and a 3D reservoir model illustrate the performance of the proposed surrogate model and history matching workflow. Compared with the MEDA using only numerical simulations, the surrogate-based AHM workflow significantly reduces the computational cost.

GeoDIN - Geoscience-Based Deep Interaction Networks for Predicting Flow Dynamics in Reservoir Simulation Models

Summary Network graphs represent a general language for describing complex systems and a framework for knowledge discovery. Graph learning is a new concept with applications emerging in biomedicine, pharmacology, smart mobility, and physical reasoning. When applied to petroleum systems, such as reservoir models, graphs provide unique differentiators for the abstraction of reservoir connectivity to facilitate “reservoir-centric” machine learning (ML) applications. In this paper, we demonstrate, for the first time, the application of geoscience-based deep interaction networks (GeoDIN) to learn complex physics relationships from 3D reservoir models for fast and accurate prediction of subsurface spatio-temporal flow dynamics. We build the network graph with embedded subsurface and physics representations and train the ML model to “act like the reservoir simulator.” We use a simulation benchmark model for two-phase incompressible flow, with approximately 1.1 million grid size, one central injector, and four corner producers. Static 3D grid properties include porosity and permeability. We use full-physics simulation output to construct the interaction network (IN) graph, where graph nodes objects (nodes) represent reservoir grid cells. We embed the feature vector combining pore, oil and water volumes, and pressure and relative permeability. The graph objects representing wells are connected with well completion factors. The producing wells have embedded oil and water production rates, while the objects representing injecting wells have embedded water injection rates. We represent graph relations (edges) with bidirectional transmissibility of the source cell. To preprocess the data for ML, we scale the graph object attributes using “min-max” normalization and we normalize the graph relation attributes using Box-Cox transformation. We train the GeoDIN framework to predict oil and water saturation dynamics in space and time. When benchmarked with full-physics simulation, the INs ran on two V100 graphics processing units and substantially accelerated the prediction phase compared to the physics-based simulator running on 70 Intel Xeon E5 CPU cores. On average, the error in GeoDIN predicted spatio-temporal distribution of oil saturation remains within 5% of full-physics simulation for 90% of model grid cells, while the error in water saturation remains within 2.5% of full-physics simulation. The spatio-temporal propagation of pressure is more sensitive to local embeddings of INs, which communicate on node-to-node information transfer. This results in a larger prediction error of the GeoDIN model when benchmarked to full-physics simulation. On average, the error distribution suggests that the great majority (90 to 95%) of grid cells fall within 10 to 30% error bound relative to full-physics simulation. The presented GeoDIN approach to network learning carries a game-changing potential for the prediction of subsurface flow dynamics. As the way forward, we will investigate the implementation of graph neural networks with automated feature learning, generalization, and scaleup.

3D Reservoir Modeling of Buzurgan Oil Field, Southern Iraq

This study aims to set up a 3D static model to characterize and evaluate Mishrif Formation which represents the main reservoir in Buzurgan Oilfield, southern Iraq. Six wells have been selected to set up structural, facies and petrophysical models of Mishrif reservoir by using Petrel Software. The structural model has been built based on the structural contour map of the top of Mishrif Formation, which derived from seismic interpretation, and by using different static algorithms in Petrel Software. The structural model showed that the Buzurgan Oilfield represents an anticlinal fold with two domes north and south separated by a depression. The petrophysical model included the porosity model and water saturation model. The results of petrophysical models showed lateral and vertical variations in the porosity and water saturation characteristics of the Mishrif reservoir. The integration between the structural and petrophysical models illustrated that the main productive zones in Mishrif reservoir are MB21, MC1 and MC2 units.

3D thermo‐hydro‐mechanical simulation of the behaviour of a naturally fractured petrothermal reservoir in deep Upper Jurassic carbonates of the Bavarian Molasse Basin – Case study Geretsried

AbstractBased on multi‐scale and multi‐disciplinary measured data, gathered at the Geretsried geothermal site, a 3D reservoir model of the deep and fracture‐controlled Upper Jurassic carbonates in the North Alpine Foreland Basin is generated in this work. An efficient methodology is developed to numerically simulate the coupled reservoir processes of fluid flow, heat transport and thermoporoelastic stresses resulting from possible geothermal doublet operating schemes with cold fluid injection and production profiles in an enhanced naturally fractured reservoir. A variety of numerical experiments is conducted to study the reactivation potential and dilation tendency of the fracture and fault system. Simulation results show the spatiotemporal evolution of the thermoporoelastic stresses and the zone affected after 50 years of geothermal doublet operation. From these simulations, the thermoelastic response of a geothermal doublet operating with 60 °C fluid injection temperature and 20 l/s flow rate translates into a maximum induced thermal stress of around 49.4 MPa near the injection well. In terms of a long‐term reservoir performance and fault and fracture reactivation potential, the findings reveal a negligible risk to a sustainable geothermal doublet operation.

A MPS-based novel method of reconstructing 3D reservoir models from 2D images using seismic constraints

While multi-point geostatistics (MPS) be used to model complex underground reservoirs, this method relies heavily on high-quality three-dimensional (3D) training images that may be difficult to acquire in real field studies. Furthermore, the original MPS technique does not include seismic data, which may noticeably improve underground reservoir predictions. Our proposed model, Se3DRCS, is a novel method to reconstruct 3D geological models from two-dimensional (2D) images and seismic information. In Se3DRCS, we are able to perform MPS-based modeling using 2D training images, rather than 3D images. These 2D images are produced by analyzing well profiles and sedimentary facies planes. After using the Se3DRCS method to obtain an initial geological model, we then generate elastic parameters and synthetic seismograms via direct sampling and convolution with the extracted seismic wavelets, respectively. By comparing the observed and synthetic seismic records, we refine the simulation results using the adaptive sampling method. The initial geological model is then updated with conditional data after each subsequent simulation iteration. When the error between the observed and synthetic seismograms falls below a reasonable threshold value, we have arrived at the best-fitting geological model. Our results indicate that the Se3DRCS method is capable of predicting the data elicited from wells with an accuracy of up to 82.88%, providing constraints on the distribution of branch channels in a delta reservoir, and reproducing local complex non-stationary geological features.

Effects of fine-scale turbidite lobe stratigraphic architecture on dynamic reservoir performance

Turbidite reservoirs with lobe stratigraphic architecture are typically encountered in deep-water environments and are present in a large number of basins. In the past, a comprehensive study to identify the key parameters governing the dynamic connectivity of turbidite channel reservoirs formed the basis for the development of the 3D reservoir connectivity factor concept allowing to account for un-modeled fine-scale stratigraphic details by computing geologically consistent field recovery discount factors. Following-up on the success of this approach, it was decided to proceed similarly with turbidite reservoirs containing lobe stratigraphic architecture, by first starting to investigate the parameters driving the performance of these reservoirs. Fine-scale reservoir models are constructed, and a large number of sensitivity-study type flow simulations are performed testing the recovery factor sensitivity to a large number of geological parameters. Analysis of the resulting data indicates that the distributary channel shale-drape coverage and its permeability, together with the lobe axis permeability, are important parameters affecting the connectivity of turbidite lobe reservoirs. Other parameters such as the lobe margin permeability, the lobe infill and lobe complex shale-drape coverages are secondary parameters. The impact of the channel incision depth, the architectural differences between proximal (more channelized) and distal (more lobate) environments and the impact of the chosen well spacing are only noticeable when shale-drape coverages are high, particularly for the distributary channel drapes. The effect of well spacing appears to be stronger in the distal environment.Through extensive visualizations of the simulated oil saturations, we further conclude that the location of by-passed oil depends on the facies permeabilities and on the shale-drape coverage. Our base-case scenario shows that lobe axes are better swept than distributary channels and lobe margins. At 100% channel shale-drape coverage, the oil located within the channels can be totally by-passed for some channels depending on well locations. When all shale-drape coverages are high (at or close to 100%), by-passed oil is observed in every facies. Contrarily to lower drape coverages, this complicated situation does not allow for a per facies localization of by-passed oil. At high shale-drape coverage, it is further noticed that the sweep efficiency of deeply incising channel models is lower than the ones simulated with shallower incising channels. Two main reasons contribute to this effect: first, the significant volume of by-passed oil that is located in the channels (deeply incising channels being bigger, less oil is swept), and second the fact that deep channels compartmentalize the reservoir more than shallow ones at high shale-drape coverages. The ensuing results are compared with those for turbidite channel reservoirs. It is concluded that shale-drape coverage has considerably less impact in lobate than in channelized systems. At equivalent shale-drape coverages, turbidite lobe reservoirs have a greater connectivity than turbidite channel reservoirs.

3D static reservoir modelling of Abu Madi paleo-valley in Baltim Field, Offshore Nile Delta Basin, Egypt

The 3D static reservoir modelling provides a good understanding for the hydrocarbon reservoir distribution and properties. This study aimed to construct a static model to study the gas-reservoir potentiality of the Messinian Abu Madi Formation in Baltim Field, offshore Nile Delta Basin. This model was constructed based on the interpretation of the thirty-seven 2D seismic sections besides the analysis of well logs data for five drilled wells (Baltim East-2, Baltim East-3, Baltim East-5, Baltim North-1 and Baltim North-2). The seismic interpretation exposed that the Abu Madi incised valley in Baltim Field is oriented NNW-SSE with width ranges from 5 to 8 km and approximately 32 km in length. The reservoir modelling revealed a relatively homogeneous reservoir facies qualities for gas production in two different zones within the Abu Madi sandstones. The trapping mechanism for these intervals is represented by pinching out in the two sides of the Abu Madi paleo-valley and bounded by two faults in the northern and southern parts of the study area.