Use of an automatic data quality control algorithm for crude oil viscosity data

When building correlations for physical properties, the data used must be quality controlled to ensure suitable performance of regression procedures and provide acceptable predictions. When using data from different sources and large datasets, this screening could become tedious and laborious unless a systematic and automated consistency check is used. is study, we had a database of almost 3000 records of PVT properties and black-oil viscosity data coming from 324 differential liberation tests performed in different commercial laboratories. eloped a procedure to “clean up” the data on a test basis, before processing it with a regression routine. We individually screened each test, identified outlying observations and removed those from the regression calculations. iteria used to discard data relied on the numerical evaluation of the first derivative of selected functions of one variable. If the physical behavior is predicted correctly, these functions should be monotonic. For example oil viscosity (observed function) should always increase as the pressure in the differential liberation tests decreases. Forward and backward derivatives were used to account for the end points. The filtered data resulting from this quality control process consisted of 2324 observations. ta were used to adapt two commonly used compositional viscosity models, Pedersen’s and Lohrenz, Bray and Clark (LBC), such that these models can be used for black-oil systems when compositional data are missing. The oil viscosity ranged from 0.13 to 78.3 cP, with pressure from 14.7 to 5602 psia (0.1–38.62 MPa) and temperature from 537 to 766 R (298–425.55 K). The oil specific gravity ranged from 0.389 to 0.921. These models were validated against an independent dataset consisting of 150 observations. The two models had lower statistical errors than currently available correlations.