In the oil industry you can make or lose money based on how good your forecasts are, so I’ve pulled together six papers that discuss different ways in which you can leverage analytics to optimize your output and more accurately predict your production performance.

Written by employees at oil and gas companies about real projects, these papers provide a good sense of what works in the real world to improve production. And there's no registration required! I they'll not only inspire you to apply analytics at your company, but provide you with a guide on how you too can make positive impact on your company’s bottom-line.

  • Production Forecasting in the Age of Big Data in Oil and Gas Industry 

    This presentation highlights the value associated with improving the accuracy of Technical Potential (TP) Forecasting.

  • Using SAS/OR® Software to Optimize the Capacity Expansion Plan of a Robust Oil Products Distribution Network

    "One of the preventive measures that supply chain planners take to avoid the possibility of
    service outage is to install additional capacities on links to guarantee satisfying the demand
    under the set of possible failure scenarios. The goal is that the resulting network (after capacity expansion decisions are implemented) should be resilient to failures-that is, the network should be able to satisfy the demand under the set of possible failure scenarios, while the total cost of capacity expansions is minimized."

  • Optimization of Refining Crude Distillation Process Unit using Process Simulation and Statistical Modeling Methods

    "In this paper we implemented a methodology to optimize the operation of a refining crude distillation unit using a combination of process simulation and statistical modeling methods. The primary objective was to estimate a set of operating targets for column pump around and bottoms stripping steam flows that maximize the unit profitability over a typical range of crude rate and crude quality operating conditions."

  • Unconventional Data-Driven Methodologies Forecast Performance In Unconventional Gas Reservoirs

    "This paper introduces innovative data-driven methodologies to forecast oil and gas production in unconventional reservoirs that, owing to the nature of the tightness of the rocks, render the empirical functions less effective and accurate. Reservoir engineers can now gain more insight to the future performance of the wells across their assets."

  • Let Oil and Gas Talk to You: Predicting Production Performance

    "This paper introduces methodologies to forecast oil and gas production by exploring implementations of the AUTOREG, ESM, and MODEL procedures in SAS/ETS®. The AUTOREG procedure estimates linear regression models when the errors are autocorrelated. The ESM procedure generates forecasts by using exponential smoothing models. Examples of the MODEL procedure arising in subsurface production data analysis are discussed. In addressing these examples, techniques for pattern recognition, implementing TREE, CLUSTER, and DISTANCE procedures in SAS/STAT® are highlighted to explicate the importance of oil- and gas-well profiling to characterize the reservoir."

  • Optimization of Gas-Injected Oil Wells

    "Artificial gas injection into aging wells boosts reservoir pressures, allowing for higher production rates. For constrained gas flows and multiple wells, the solution of this problem becomes difficult and time intense. With SAS/OR optimization techniques, a scalable (from 1 to n well) solution can be used to provide quick results. This paper provides background on artificial injection to orient the reader, theory on the mathematical formulation of the optimization, and the SAS code with results."

Advanced analytics are key in driving better results in the age of the digital oil field.  Find out more about using analytics in the Oil & Gas industry here.

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About Author

David Pope

Technical Leader, Senior Manager US Energy

David leads the pre-sales technical team for SAS US Energy which solves business problems in the Oil & Gas and Utilities industries using advanced analytics. He earned a BS in Industry Engineering and a Computer Programming Certificate from North Carolina State University. Furthermore, he has over 27 years of business experience working with SAS across R&D, IT, Sales and Marketing in the Americas and Europe. He is an expert in working with data and producing insights through the use of analytics. David has presented at SAS Global Forum, the 2012 SAS Government Leadership Summit, IBM’s Information on Demand(IOD), EMC World, CTO Summit Conferences, is the author of the book: "Big Data Analytics with SAS", and he currently holds 11 patents for SAS in several countries: US, CA, Norway, UK, China, and Hong Kong.

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