• The future of shale

      Malin, Michael A.; Vander Naald, Brian P.; Little, John; Tichotsky, John; Reynolds, Douglas (2016-05)
      This project examines the various drivers that led to the U.S. shale oil revolution in order to predict its place in the energy industry going forward and to analyze its effects on Alaska. The shale boom flooded the market with oil causing a dramatic decrease in crude oil prices in late 2014. With this price drop threatening to send Alaska into an economic recession, the future of shale should be of primary concern to all Alaskans as well as other entities that rely heavily on oil revenue. The primary driver leading to the shale revolution is technology. Advances in hydraulic fracturing, horizontal drilling, and 3D seismic mapping made producing shale oil and gas possible for the first time. New technologies like rotary steerable systems and measurements while drilling continue to make shale production more efficient, and technology will likely continue to improve. Infrastructure helps to explain why the shale revolution was mostly an American phenomenon. Many countries with shale formations have political infrastructure too unstable to risk shale investment. Capital infrastructure is a primary strength of the U.S. and also helps to explain why shale development didn't find its way up to Alaska despite having political stability. Financial infrastructure allowed oil companies to receive the funding necessary to quickly bring shale to the market. The final driver explored is crude oil prices. High oil prices helped spark the shale revolution, but with the recent price crash, there is uncertainty about its future. With production costs continually falling due to technology improvements and analysts predicting crude oil prices to stabilize above most project breakeven points, the future of shale looks bright.
    • Rate transient analysis and completion optimization study in Eagle Ford shale

      Borade, Chaitanya; Patil, Shirish; Inamdar, Abhijeet; Khataniar, Sanatanu (2015-08)
      Analysis of well performance data can deliver decision-making solutions regarding field development, production optimization, and reserves evaluation. Well performance analysis involves the study of the measured response of a system, the reservoir in our case, in the form of production rates and flowing pressures. The Eagle Ford shale in South Texas is one of the most prolific shale plays in the United States. However, the ultra-low permeability of the shale combined with its limited production history makes predicting ultimate recovery very difficult, especially in the early life of a well. Use of Rate Transient Analysis makes the analysis of early production data possible, which involves solving an inverse problem. Unlike the traditional decline analysis, Rate Transient Analysis requires measured production rates and flowing pressures, which were provided by an operator based in the Eagle Ford. This study is divided into two objectives. The first objective is to analyze well performance data from Eagle Ford shale gas wells provided by an operator. This analysis adopts the use of probabilistic rate transient analysis to help quantify uncertainty. With this approach, it is possible to systematically investigate the allowable parameter space based on acceptable ranges of inputs such as fracture length, matrix permeability, conductivity and well spacing. Since well spacing and reservoir boundaries were unknown, a base case with a reservoir width of 1500 feet was assumed. This analysis presents a workflow that integrates probabilistic and analytical modeling for shale gas wells in an unconventional reservoir. To validate the results between probabilistic and analytical modeling, a percent difference of less than 15% was assumed as an acceptable range for the ultimate recoverable forecasts. Understanding the effect of existing completion on the cumulative production is of great value to operators. This information helps them plan and optimize future completion designs while reducing operational costs. This study addresses the secondary objective by generating an Artificial Neural Network model. Using database from existing wells, a neural network model was successfully generated and completion effectiveness and optimization analysis was conducted. A good agreement between the predicted model output values and actual values (R² = 0.99) validated the applicability of this model. A completion optimization study showed that wells drilled in condensate-rich zones required higher proppant and liquid volumes, whereas wells in gas-rich zones required closer cluster spacing. Analysis results helped to identify wells which were either under-stimulated or over-stimulated and appropriate recommendations were made.