Compositional Tracking: Predicting Wellstream Compositions in Tight Unconventionals

Abstract

Wellstream compositions are needed for a range of critical engineering applications but it is impractical and too expensive to acquire these compositions frequently (e.g. daily) for every well in an unconventional basin. Detailed PVT studies that provide wellstream compositions can easily cost 25,000–50,000 USD and are typically performed on less than 1% of the wells in a "shale" basin. This paper provides methods to predict wellstream compositions daily in wells without detailed PVT analysis by using readily available production and well test (fluid sampling) data. The three different methods presented all leverage a field-wide equation of state (EOS) model together with a varying degree of available production and well test data. The EOS model used should honor all the available fluid data in the relevant field, region, or formation, and should be developed based on as many detailed PVT studies as possible. The difference between the three different methods is the amount and type of data typically available, ranging from production data only to extensive compositional well test data. This paper shows that accurate wellstream compositions can be predicted by using a field-wide EOS model together with production and well test data. The accuracy of the different methods depends on (1) the amount of available measured data, (2) the quality of the available measured data and (3) how well the EOS model captures the phase characteristics of the relevant fluid. The proposed methods provide a tool to consistently and accurately calculate daily wellstream compositions for all wells in a cost-efficient manner. EOS models enable convenient and flexible calculations for describing phase behavior of petroleum fluids. However, to use an EOS model, three input parameters are needed: temperature, pressure and composition. In unconventionals, temperature and pressure are almost always available, but wellstream composition is not. The purpose of this work is to make wellstream compositions readily available for unconventional fields with thousands of wells, and thereby unlock the potential for EOS model utilization.