Use of Artificial Intelligence to Measure Gas Flow Rate, Bongkot Asset

Ketmalee, Thanapong (PTTEP) | Sirisawadwattana, Jutaratt (PTTEP) | Piyajunya, Tanunya (PTTEP) | Ngamkamollert, Krit (PTTEP) | Bandyopadhyay, Parthasarathi (PTTEP) | Whangkitjamorn, Jugkapun (PTTEP)

OnePetro 

Abstract

Greater Bongkot North is a gas field located in Gulf of Thailand and on production since 1993. Most of the old wellhead platforms (30%) lack remote well test facilities which requires personnel visits for any well test measurement. Often, well testing in these platforms get lower priority compared to other operations in a matured field. This project implemented artificial intelligent (AI) technique to estimate gas rate from other available engineering and geological parameters.

A new approach using machine learning was applied to estimate gas production rate where actual measurements are not available. Actual production well test data was used to train the model. Input parameters used were:

Surface facility information

Fluid properties

Production condition

Geological setup

A blind test on the subset of historical data showed a level of confidence (R2) value of 0.93. This provided confidence to proceed with a full field pilot. A pilot was conducted during January to May 2018. The area of pilot was spread across various geological, operating and surface condition setups to reduce sampling bias. The pilot demonstrated the following use cases:

Improved prediction accuracy in wells with no recent test, achieving primary object of model.

Detection of well behavior changes: The model could detect changes in well behavior without human intervention much before the trends become obvious for engineers to detect.

Improved potential estimation in wells with leaks in wellhead chokes where conventional analysis followed in Bongkot is not possible due to improper wellhead shut-in pressure measurement.

Improved efficiency with production allocation: The conventional method requires significant time (40-80 person hours per month) to make the data available for production allocation. This can be shortened significantly by use of this method

In essence, this project demonstrated the potential use of artificial intelligent to improve efficiency in a matured gas field operating under marginal conditions.