Production monitoring using artificial intelligence, APLT asset

Society of Petroleum Engineers - SPE Intelligent Energy International 2012

Volumn 1, Issue , 2012, Pages 80-94

  Olivares, G ^a   Escalona, C ^b   Gimenez, E ^b  

^a PEMEX   (Mexico)

^b SCHLUMBERGER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE TECHNIQUES; EARLY DETECTION SYSTEM; HIGH FREQUENCY DATA; MULTI-PHASE FLOW METERS; NODAL ANALYSIS; OPERATIONAL CONDITIONS; OPERATIONAL COSTS; OPERATIONAL ISSUES; PRODUCTION MONITORING; PRODUCTION WELLS; PROXY MODEL; PUMPING PROCESS; REAL-TIME DATA; SOUTHWEST MARINES; WELL PRODUCTION; WELL TEST; WELL TEST DATA;

ARTIFICIAL INTELLIGENCE; COMMUNICATION; HYDROCARBONS;

WELL TESTING;

EID: 84861633340     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Ashford, F.E. and Pierce, P.E. 1974. The Determination of Multiphase Pressure Drops and Flow Capacities in Down-Hole Safety Valves (Storm Chokes). Paper SPE 5161 presented at SPE Annual Fall Meeting, Houston, Texas, 6-9 October.
2. Gilbert, W.E. 1954. Flowing and Gas Lift Well Performance.
3. Patterson, D.W. 1998. Artificial Neural Networks: Theory and Applications
4. PEMEX. 2008. Neural Network Simulator of APLT.
5. De Jonge, G.J. and Stundner, M. 2002. How Routine Reservoir Surveillance with Neural Networks and Simplified Reservoir Models Can Convert Data Into Information. Paper SPE 78334 presented at European Petroleum Conference, Aberdeen, United Kingdom, 29-31 October.
6. Stundner, M. Al-Thuwaini J.S. 2001. How Data Driven Modeling Method Like Neural Networks Can Help To Integrate Different Types of Data Into Reservoir Management. Paper SPE 68163 presented at SPE Middle east oil show, Bahrain17-20 March.