Multi-scale modeling of inertial flows through propped fractures

Non-Darcy flows are expected to be ubiquitous in near wellbore regions, completions, and in hydraulic fractures of high productivity gas wells. Further, the prevailing dynamic effective stress in the near wellbore region is expected to be an influencing factor for the completion conductivity and non-Darcy flow behavior in it. In other words, the properties (fracture permeability and β-factor) can vary with the time and location in the reservoir (especially in regions close to the wellbore). Using constant values based on empirical correlations for reservoirs/completions properties can lead to erroneous cumulative productivity predictions. With the recent advances in the imaging technology, it is now possible to reconstruct pore geometries of the proppant packs under different stress conditions. With further advances in powerful computing platforms, it is possible to handle large amount of computations such as Lattice Boltzmann (LB) simulations faster and more efficiently. Calculated properties of the proppant pack at different confining stresses show reasonable agreement with the reported values for both permeability and β-factor. These predicted stress-dependent permeability and β-factors corresponding to the effective stress fields around the hydraulic fractured completions is included in a 2D gas reservoir simulator to calculate the productivity index. In image-based flow simulations, spatial resolution of the digital images used for modeling is critical not only because it dictates the scale of features that can be resolved, but also because for most techniques there is at least some relationship between voxel size in the image data and numerical resolution applied to the computational simulations. In this work we investigate this relationship using a computer-generated consolidated porous medium, which was digitized at voxel resolutions in the range 2-10 microns. These images are then used to compute permeability and tortuosity using lattice Boltzmann (LB) and compared against finite elements methods (FEM)simulation results. Results show how changes in computed permeability are affected by image resolution (which dictates how well the pore geometry is approximated) versus grid or mesh resolution (which changes numerical accuracy). For LB, the image and grid resolution are usually taken to be the same; we show at least one case where effects of grid and image resolution appear to counteract one another, giving the mistaken appearance of resolution-independent results. For FEM, meshing can provide certain attributes (such as better conformance to surfaces), but it also adds an extra step for error or approximation to be introduced in the workflow

Drilling Performance Monitoring and Optimization: A Data-driven Approach

Abstract Drilling performance monitoring and optimization are crucial in increasing the overall NPV of an oil and gas project. Even after rigorous planning, drilling phase of any project can be hindered by unanticipated problems, such as bit balling. The objective of this paper is to implement artifcial intelligence technique to develop a smart model for more accurate and robust real-time drilling performance monitoring and optimization. For this purpose, the back propagation, feed forward neural network model was developed to predict rate of penetration (ROP) using diferent input parameters such as weight on bit, rotations per minute, mud fow (GPM) and diferential pressures. The heavy hitter features identifcation and dimensionality reduction are performed to understand the impacts of each of the drilling parameters on ROP. This will be used to optimize the input parameters for model development and validation and performing the operation optimization when bit is underperforming. The model is frst developed based on the drilling experiments performed in the laboratory and then extended to feld applications. From both laboratory and feld test data provided, we have proved that the data-driven model built using multilayer perceptron technique can be successfully used for drilling performance monitoring and optimization, especially identifying the bit malfunction or failure, i.e., bit balling. We have shown that the ROP has complex relationship with other drilling variables which cannot be captured using conventional statistical approaches or from diferent empirical models. The data-driven approach combined with statistical regression analysis provides better understanding of relationship between variables and prediction of ROP