Data-Driven Production-Impact Assessment During Unplanned Facility-System Events

Topics: Data and information management Facilities planning and maintenance Risk management/decision-making
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A data-driven approach to successfully analyze and evaluate production-fluid impact during facility system divert events is presented. The work flow effectively identifies opportunities for prompt event mitigation and system optimization. The methodology used includes data streaming, advanced computations on high-frequency data, visualization, data mining, and rules extraction. The paper demonstrates that applied data-driven analytics led to learnings and observations that had a positive effect on the management of facility systems during divert events.


The subject field is a heavy-oil reservoir, using steam for enhanced oil recovery. The field architecture is set up so that the active wells separate the produced liquids and produced (casing) gas at the wellhead. The operating objective is to minimize the pressure in the casing gas-collection system to maximize the liquid production from the wells. The casing gas flows from each individual well through a check valve into a common network and then enters one of the two casing-gas stations in the field. The gas is cooled, which removes a large volume fraction of condensables (mostly water) from the gas stream. Next, the gas flows into the inlet of the gas-plant compressors. From the compressors, the gas will follow paths that depend on the operating conditions. During normal field operations, the gas flow goes into the plant. The plant removes hydrogen sulfide so that the gas can be incinerated in the steam generators.

When the plant or the steam generators are not available to run because of planned or unplanned events, the discharge of the compressors is redirected into the casing-gas-collection network. This mode of operation is called “divert.” Operating in divert mode results in an increase in the casing-gas-network pressure with respect to normal conditions. This high pressure on the casing of the well is known to reduce the production volume.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173993, “Data-Driven Analytics for Production-Impact Assessment During Unplanned Facility-System Events,” by Andrei S. Popa, Hugo Leon, Juan Medel, Tuan Nquyen, Steve Cassidy, and Dallas Tubbs, Chevron, prepared for the 2015 SPE Western Regional Meeting, Garden Grove, California, USA, 27–30 April. The paper has not been peer reviewed.
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Data-Driven Production-Impact Assessment During Unplanned Facility-System Events

01 October 2015

Volume: 67 | Issue: 10