... Abstract Machine learning application in the oil and gas industry is rapidly becoming popular and in recent years has been applied in the optimization of production for various reservoirs. The objective of this paper is to evaluate the efficacy of advanced machine learning algorithms...

...) and microstructure (Scanning Electron Microscopy). Wireline measurements include the triple combo and the sonic logs. Principal Component Analysis and K-means (as unsupervised machine learning algorithms) were applied to both datasets to cluster and classify different rock types. In parallel, the petrophysical...

... prediction at an average true vertical depth of 6000 ft showed 26.7% or 4292 wells have more than 150 °F and can be converted for geothermal production. The shortest distance heuristic algorithm was used to calculate the shortest distance of each well to any city in Oklahoma. Before conversion, an evaluation...

.... Obstacles to creating production type curves are attributed to the complexities in heterogeneous reservoir properties, improved drilling and completion techniques, and evolving production and operation procedures. This paper will highlight improvements to a proposed machine-learning algorithm to generate...

... essential. This paper aims to implement machine learning algorithms to predict LWD data at the bit. The results of the model can be used to perform real-time analysis that considers the alterations in petrophysical properties, lithologies and rock strengths while drilling, without the drawbacks of LWD...

... application data-driven method dataset Oil Recovery evaluation Injection Rate Sheng Artificial Intelligence Engineering information flooding shale oil reservoir oil phase enhanced recovery regression gas injection injection oil reservoir algorithm reservoir society of petroleum engineers...

... for induced fractures formations. A decision was made to have one hidden layer in the network with ten neurons in the hidden layer. Since there are many training algorithms to choose from, it was necessary to choose the best algorithm for this specific data set. Ten different training algorithms were tested...

... distribution among different perforation clusters over time is presented. Ideally, slurry flow is equal between perforation clusters and, at least, constant in time, but the reality is very different. The interpretation workflow is based on proprietary algorithms within a general wellbore software platform...

... for a reservoir in the Anadarko basin. The Dykstra-Parsons coefficient computed indicates a highly heterogeneous and complex reservoir. This work compares different upscaling algorithms for both primary and secondary reservoir depletion processes. Also, the effects of different boundary conditions were tested...

.... In this paper a reservoir-performance based algorithm proposed by Ozkan et al. (2003) is applied to optimize the gas production and shut-in periods of plunger lift operation. The objective of the optimization is to maximize gas production with the condition that the liquid loaded during production can...

... & Gas discharge pressure threshold stress pattern Artificial Intelligence segmentation information algorithm intake pressure wellhead pressure probability society of petroleum engineers spe 164497 SPE 164497 Real-Time Data and Expert System Combine to Develop Artificial Lift Predictive...

... Abstract Reservoir characterization and forecasting require construction of reliable and history matched reservoir models. In gradient-based automatic history matching procedure, most of the CPU time is spent on the sensitivity calculation. When the adjoint algorithm is used to calculate...

... effectiveness. Field implications are discussed. Upstream Oil & Gas control variable genetic algorithm optimization problem interval pressure operation Simulation time step algorithm Artificial Intelligence gas reservoir experiment aquifer reservoir valve well length proxy model...

... the first year 12 months of production. The data included producing wells and dry holes. Potential input variables were tested against the output to determine the best variables for training. First, a fuzzy ranking algorithm selected Woodford thickness and TOC. A secondary correlation study also...

... well performance reservoir characterization gas condensate blasingame spe 80907 case 16 algorithm spe 80907 ipr behavior pvt measurement equation of state del castillo flowrate grace algorithm Introduction — Solution Gas-Drive Systems The first presentation of an inflow performance...

... be retrained with multi well data to improve its scope and applicability. Unfortunately, some efforts to upgrade can result in choosing to use sub-optimal training algorithms because of the need to deal with an extensively increased data size. The consequence is a reduction in predictive accuracy. To improve...

... the success of such a program. hydroxypropyl guar algorithm upstream oil & gas codell restimulation evolve spe 67211 peak rate fluid type polymer original completion well intervention hydraulic fracturing production enhancement artificial intelligence codell fluid system drillstem...

... well performance, surface line, and facility models. For each iteration in the optimization model oil rate from the field production model is computed. Two major impediments to the rigorous optimization of large producing fields are the effectiveness of the optimization algorithm and the cost...

... calculations. Core data are seldom available on exploration tests, especially in carbonate reservoirs. A flexible algorithm defines formation resistivity factor ( F R ) boundaries within which log measurements from carbonates occur. Evidence shows the n exponent in carbonate reservoirs to be strongly...

... − d p m e a s ( t j ) d ln t ) = 0. In this work, we employ analytical expressions for the partial derivatives with respect to each reservoir or well parameter. Parameter optimization is achieved using a public-domain Levenberg-Marquardt 4 algorithm from Argonne...