Analysis and Modeling of an Extensive Data Material for the Compositional Variation with Depth in a North Sea Reservoir

Abstract Detailed compositional data is presented for 43 fluid samples taken at different depths in a 359 m North Sea reservoir fluid column with a gas cap on top and an underlying oil zone. The data enables the depth of the gas-oil contact to be determined quite accurately. A common equation of state model has been developed for the fluids and depth gradient simulations carried out. In the gas zone, gravitational forces can explain most of the observed compositional variation. In the oil zone, the compositional variation is larger than can be explained by the gravitational effect. There is a need to also account for the heavy hydrocarbons orienting themselves towards the warmer bottom zone and lighter hydrocarbons towards the colder top zone. The Haase model, which is based on the theory of irreversible thermodynamics, is used to describe how the vertical temperature gradient affects the compositional gradient. It requires knowledge of the absolute partial molar enthalpies of the individual components, of which it thanks to the extensive data material has been possible to give a good estimate. With the presented model concept, it is possible to represent the compositional gradient over the entire fluid column including the depth of the gas-oil-contact and the variations in gas/oil ratio and saturation point with depth. The determined model parameters are also expected to be applicable to other petroleum reservoirs.

Authors

Publication Details

Published
2024-10-20
DOI
https://doi.org/10.2118/223251-ms
Primary Topic
Hydrocarbon exploration and reservoir analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis and Modeling of an Extensive Data Material for the Compositional Variation with Depth in a North Sea Reservoir

Kim Steenstrup Pedersen, H. P. Hjermstad
Hydrocarbon exploration and reservoir analysis
article

Analysis and Modeling of an Extensive Data Material for the Compositional Variation with Depth in a North Sea Reservoir

Kim Steenstrup Pedersen, H. P. Hjermstad
article en

Abstract

Abstract Detailed compositional data is presented for 43 fluid samples taken at different depths in a 359 m North Sea reservoir fluid column with a gas cap on top and an underlying oil zone. The data enables the depth of the gas-oil contact to be determined quite accurately. A common equation of state model has been developed for the fluids and depth gradient simulations carried out. In the gas zone, gravitational forces can explain most of the observed compositional variation. In the oil zone, the compositional variation is larger than can be explained by the gravitational effect. There is a need to also account for the heavy hydrocarbons orienting themselves towards the warmer bottom zone and lighter hydrocarbons towards the colder top zone. The Haase model, which is based on the theory of irreversible thermodynamics, is used to describe how the vertical temperature gradient affects the compositional gradient. It requires knowledge of the absolute partial molar enthalpies of the individual components, of which it thanks to the extensive data material has been possible to give a good estimate. With the presented model concept, it is possible to represent the compositional gradient over the entire fluid column including the depth of the gas-oil-contact and the variations in gas/oil ratio and saturation point with depth. The determined model parameters are also expected to be applicable to other petroleum reservoirs.

Life below water
Openalex Percentile: Top 80%
Hydrocarbon exploration and reservoir analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.