Choosing an appropriate technique for upscaling the permeability of the discreet fracture network (DFN) model is vital for maximizing recovery from naturally fractured reservoirs (NFR). Flow -based upscaling is accurate, yet it is computationally expensive. Analytical (i.e. Oda method) upscaling is computationally efficient; however, it is only accurate for well-connected fractures. The objective of this paper is to analyze the performance of the newly developed Oda corrected method which addresses issues associated with previous upscaling methods. This research was executed by using a commercial numerical simulator and by using data set from the Teapot Dome Reservoir. Furthermore, DFN modeling was used to generate different realizations for the fracture network. Consequently, sensitivity analysis was performed through a realisti c uncertainty quantification to generate a base case and 6 different DFN realizations. The main parameters used for this study are fracture length and intensity. Afterwards, the fracture permeability corresponding to each DFN realization was upscaled using the above-mentioned methods. Finally, differences between the upscaling methods were evaluated and analyzed using flow-based upscaling as the criterion. Indeed, the analysis revealed that the new Oda corrected method can calculate the equivalent permeability tensor with adequate accuracy. However, it overestimates the permeability when fracture networks are below the percolation threshold and/or when fractures length is large. Hence, this method is recommended for networks with moderate to high intensity. Furthermore, it has been deduced that fracture length has a great impact on the connectivity of fractures, albeit its effect on permeability is limited by fracturing density. Additionally, it has been found that the length of fractures has an immense impact on the anisotropy ratio and control the occurrence of water bypassing, which were not captured by the Oda method.

This study describes an application of a compositional single well simulator to analyse well tests in gas-condensate reservoirs. An important aspect of this application for gas-condensate well tests is accurate fluid property prediction during the multi-phase flow regime, which occurs in the near-well region. The simulator can also be used to understand the impact of liquid drop-out and fracture flow on well productivity. Hydraulic fracturing improves the economics of wells drilled in tight reservoirs. However, the operation involves a significant amount of expenditure. In recent years this technique has also been used to stimulate gas-condensate reservoirs by creating a flow conduit through the condensate banking near the well. Thus, it is crucial to keep a fracture as small as possible. In practice it has been proved that a short, wide fracture can provide much higher production than the traditionally pursued narrow, long fracture. The workflow in this study contains compositional simulation of a single well in a tight gas-condensate reservoir, which is used to generate transient pressure data for well test analysis and interpretation to predict multi-phase flow behaviour, and to analyse productivity impairment due to condensation. Simulation models were then further modified to study the impact of various hydraulic fractures on the well productivity index (PI), which is defined as the ratio of production rate (constant) divided by the pressure drop across the reservoir. PIs for fractured cases are compared with respect to the non-fractured base case. Streamline simulation of the fractured gas-condensate reservoir was also included in the study to allow visualization of the flow profile in and around the hydraulic fracture.

Optimum well placement in intelligent fields, using previously developed optimal control methods to maximize net present value (NPV), is becoming practical with recent advances in technologies as well as their applications to the petroleum industry. To efficiently use these methods in an intelligent field, an assessment of its economic aspects and its performance, especially in reservoirs with high degree of heterogeneity (uncertainty), must be made. By using such integrated workflows, mature and new field can be developed better. The workflow could be used as a reliable tool for improving the decision-making process. There are multiple optimization techniques used in the industry for optimizing well placement (e.g. direct and gradient optimization). With the use of reservoir simulation case study, this paper aims to provide a comparative performance analysis of multiple optimization techniques. To make the evaluation stronger and more application to a real-world problem, the model selected for this study has a high degree of geological uncertainty and constraints for computation time, infrastructure and complexity to decide on optimal well placements. Having a better understanding on the uncertainties in geology lead to more robust decisions in reservoir management. Right strategy especially helps in optimizing larger scale, million-cell model simulations enabling practical implementation of reservoir simulation coupled with optimization. Optimum well placement in complex reservoirs requires a complete grasp of optimization methods, key factors and constraints but most importantly the effect of geological uncertainty. A lack of awareness of optimization algorithms and their applications by engineers is a drawback in this process. In addition, complete evaluation of geological uncertainty is another challenge. This study provides an understanding and clarification to serve as a guideline on optimization practices by outlining the significant components in the process.

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The objective of this work is to describe a comprehensive approach integrating seismic data processing and sets of wireline logs for reservoir characterization of one of the tight gas plays of the Dnieper-Donets basin. This paper intends to discuss a case study from seismic data processing, integrating seismic attributes with formation properties from logs in a geocellular model for sweet spot selection and risk analysis. The workflow during the project included the following steps.Seismic data 3D processing, including 5D interpolation and PSTM migration.Interpretation of limited log data from 4 exploration and appraisal wells.Seismic interpretation and inversion.Building a static model of the field.Recommendations for drilling locations.Evaluation of the drilled well to verify input parameters of the initial model. The static model integrated all available subsurface data and used inverted seismic attributes calibrated to the available logs to constrain the property modelling. Then various deterministic and stochastic approaches were used for facies modeling and estimation of gas-in-place volume. Integrating all the available data provides insights for better understating the reservoir distribution and provided recommendations for drilling locations. Based on the combination of the geocellular model, seismic attributes and seismic inversion results, the operator drilled an exploration well. The modern set of petrophysical logs acquired in the recently drilled well enforced prior knowledge and delivered a robust picture of the tight gas reservoir. The results from the drilled well matched predicted formation properties very closely, which added confidence in the technical approach applied in this study and similar studies that followed later. It is the fork in the road moment for the Dnieper-Donetsk basin with huge tight gas potential in the region that inspires for exploration of other prospects and plays. A synergy of analytical methods with a combination of seismic processing, geomodeling, and reservoir characterization approaches allowed accurate selection of the drilling targets with minimum risk of "dry hole" that has been vindicated by successful drilling outcome in a new exploration well.