... the quartz minerals was approximately 60% less than that in clay minerals colonized next to the quartz under the same load. In the next step, we compared these mechanical characterizations with estimates obtained from the effective medium models. We observed measurable uncertainties (more than 15% depending...

... to the phase field model developed to approximate the fracture toughness of the rock tested. Scratch tests are performed on samples obtained from Eagle Ford formation. The experiments are conducted in short transverse and divider orientations. The crack formations are studied under the progressive load...

... process we utilize a trained Deep Recurrent Neural Network (RNN) to approximate the reservoir simulator flows, which are subsequently used to compute the economic performance of a drilling policy through its discounted cash flows. The RNN is trained and tested on a set of reservoir simulator runs over...

..., I used supervised learning for approximating and compressing the traveltime tables by artificial neural networks. Second, I used unsupervised learning for solving the eikonal equation. I used fully-connected neural networks for solving both problems. For the first problem, I used traveltimes...

... Most of the time and even for complex reservoir types and shapes, the 2D approximation of the structure is adequate (i.e. it is possible to find one direction along which the reservoir exhibits little change) and the 2D inversions can accurately map the reservoir. However, if the resistivity...

...-dependent permeability coefficients. A perturbation technique is applied to mathematically treat the described nonlinearity to solve for the coupled equations of pore fluid flow and rock stress under steady-state flow. The good match between the obtained analytical approximations for productivity index...

... to create an alternative approach to determining BHP. After investigation, I determined that using data from different combinations of water-gas ratio (WGR), wellhead pressure (WHP), and gas rate allowed me to create a correlation to approximate the BHP of any given well on any given producing day...

... machine learning Artificial Intelligence history matching Upstream Oil & Gas reservoir simulation covariance matrix ar algorithm Optimization Method uncertainty quantification approximation l-gmm approximation Bayesian Inference objective function conditional realization map...

... as a design reference. It is also observed that the outer string in a dual-string system tends to withstand more moments because of greater stiffness. Proper application of this new dual-string buckling model design can help reduce costs. Artificial Intelligence approximation length change potential...

... on phase rates, as well as pressure and saturation distributions across the system is studied. reservoir simulation flow in porous media drillstem/well testing compressibility outer boundary Drillstem Testing Fluid Dynamics approximation Artificial Intelligence multiphase flow anomalous...

... approximate conditional realizations using the distributed Gauss-Newton (DGN) method together with a multiple local Gaussian approximation technique. Results are compared with those obtained from other approaches such as Randomized Maximum Likelihood (RML), Ensemble-Kalman-Filter (EnKF), and Markov-Chain...

... Upstream Oil & Gas Thomsen practical application characteristic modeling effective medium theory mathematical model Reservoir Characterization approximation tensor Applicability effective elastic property aspect ratio Artificial Intelligence comparison body porous rock...

... the full-order state solution such as pressure and saturation, evaluate the full-order nonlinear term before projecting it onto a reduced subspace. Therefore, we developed a reduced-order reservoir model based on a discrete empirical interpolation method (DEIM) to approximate nonlinear phase potential...

... the downside effects for a chemical EOR process. The method is a stochastic approximate dynamic programming approach to decision making. It is based on a forward simulation coupled with a recursive algorithm which produces the near-optimal policy. It relies on Monte Carlo simulation to produce convergent...

... to production data prediction analysis for unconventional assets. reservoir surveillance upstream oil & gas production monitoring reservoir simulation approximation desorption effect density approximation apparent gas permeability shale gas compressibility isotherm gas-slippage factor sol...

..., such as piece-wise linear approximations, is an algorithmic issue; not a fundamental defect. Specifically, an adequate algorithm to transform the piecewise-continuous sampled data into the Laplace space and an appropriate numerical Laplace inversion algorithm capable of processing the exponential contributions...

... may result in significant error. The new method enables petroleum engineers to calculate the average reservoir pressure accurately for every reservoir type by integrating transient, production, and static data. Our technique calculates a robust approximation of the drainage area based...

... against several analytical solutions and also demonstrate the approach's ability to predict complex fracture systems with example of multiple interacting fractures. artificial intelligence approximation numerical simulation complex reservoir criterion crack geometry upstream oil & gas...

...-Poiseuille flow is studied. We present a set of analytically flow equations for flow between parallel plates/slots in a most compact form, and use this set as a basis for developing simpler flow equations. The new equations approximate the full analytical solution over a wide range of flow rates...

..., guidelines are proposed for the impulse tests analysis, which combine the use of the shut-in pressure change, the superposition time pressure derivative, and the second drawdown derivative response. With the aim to illustrate the validity of the new approximations, several cases of tests with short time...