Cockrell School of Engineering
The University of Texas at Austin


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Graduate Seminar: Sidd Gupta

Date

Monday, February 10, 2014

Time

03:00pm - 04:00pm

Location

CPE 2.208

Description

Sidd Gupta, Production Solution Champion at Schlumberger will give a talk entitled "Minimizing Uncertainty in Shales by Integrating Cross Domain Data" as part of the Claude R. Hocott Graduate Seminar Series.

Bio: Sidd Gupta is the Production Solution Champion at Schlumberger. In the last 7 years with Schlumberger he has been involved in Borehole Production Engineering, Integrated Asset Modeling and Petroleum Engineering Consulting and Services. Currently he is working on creating and delivering integrated workflows and solutions for unconventional resources. He also has a long affiliation with SPE, writing papers, serving on the young professional board and acting as a mentor to PE students. Sidd has a BS in Petroleum Engineering from Indian School of Mines and a MS in Petroleum Engineering from The University of Texas at Austin (Hook ‘em)

Abstract: There are several aspects of unconventional reservoirs that make them hard to understand. The physics of flow in such ultra-low permeability reservoirs is essentially unknown. Uncertainties and well counts are large, Information along horizontal bore is minimal and production variability is huge. This presentation uses production and completion data to answer several pertinent questions about unconventional reservoirs.

High well counts increases the volume of data, which makes it overwhelming to analyze on a typical spreadsheet. Data Mining and Data Analytics is used to recognize underlying trends, determine sweet spots and identify optimum completion parameters. Smart well clustering is also employed to create sets of wells that are grouped together in a logical fashion to generate type curves. Most wells, especially in the newer plays, exhibit transient flow regimes so fit-for-purpose decline curve analysis is applied to get an accurate estimate of the production forecast. Some wells which are not performing optimally have to be re-stimulated, and typically, there is only a 15% chance that the right candidates are selected. Production and completion data is analyzed using a workflow to screen and significantly lower the number of candidates under consideration.

A large percentage of studies focus on the reservoir only, however, production problems are not always about the reservoir. A comprehensive review of a large well network in Eagle Ford created from public data like coordinates, well test and pipeline dimensions, shows that slugging, liquid loading, network bottlenecks etc. affect the system performance as a whole and can significantly lower production if not corrected. This presentation will introduce the concept of multi-level data analysis from Unconventional Reservoirs and demonstrate the results with over 15000 wells from Barnett, Bakken and Eagle Ford.