Title:

The Effect of Noise Data in QA and QC to Determine Deliverability Curve

Authors:

Hendra Ardi KURNIAWAN, Clinton SIHOMBING, Muhammad THASRIL, Ermawan ISYAHTORO, Achmad Djumarma WIRAKUSUMAH

Key Words:

production test, total mass flow rate, enthalpy, gas content, noise data, deliverability curve, lesson learn

Conference:

Stanford Geothermal Workshop

Year:

2018

Session:

Field Studies

Language:

English

Paper Number:

Kurniawan

File Size:

1430 KB

View File:

Abstract:

A production test is one of the most important and crucial thing that necessary to do before the well is produced. The production test will be represented the well flow characteristic in form of total mass flow rate, fluid enthalpy, heat flow, and dryness. Furthermore, the reservoir characteristic and well capacity also will be known. The result of production test will be used as a source to develop the geothermal field. Therefore, the measurement accuracy of production test must be corrected carefully. Geothermal fluid in Indonesia is commonly in the form of steam phase, water phase or both of them. There are some impurities that follow carried produce such as gas content. Gas content that participates in the production along with the geothermal fluid in the steam. The gas content may affect the result of mass flow measurement that will have implications relating to the total mass flow and enthalpy. It is necessary to calculate the gas content, so the accurate total mass flow rate and enthalpy can be obtained. In the production test, noise data is a common thing to be happened, the noise data will affect the accuracy of the total mass and enthalpy. Although it can be minimized, its existence will always occur in the production process. To minimize the effect of the noise data, processing data is needed to get accurate results. This study aims to provide sample cases of geothermal well production test in Indonesia. This case study illustrates the existence of noise data on the accuracy level based on the Russel James equation. In addition, to give lesson learn of production tests in the future related to the obstacles that occur in the field so as to give the best results to be interpreted.


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