Modelling natural gas resource uncertainties and regional gas markets: a review of current models and an introduction to a new field-level gas production and trade model

Abstract:

This UKERC working paper reviews the literature on modelling natural gas demand and supply. This includes modelling natural gas markets in isolation, and as part of its role in the wider energy system.

This review is part of the work on a new, global gas model at the Institute for Sustainable Resources at University College London, through a UKERC PhD Studentship. The focus of the new model is on global gas production and trade, and its coupling with the TIMES Integrated Assessment model at University College London (TIAM-UCL) to represent gas demand.

Trade-offs: simplifying complexity without diverging from reality

The main section of this working paper provides a review of existing methods which model both supply chain and demand dynamics of natural gas (Part 1: recoverable volumes and corresponding costs of natural gas; Part 2: wider energy-system models; Part 3: natural gas market models). As with any modelling, it was found that there is always a trade-off between necessary simplifications, and the uncertainties and complexities which surround energy-economic-environmental systems.

In Part 1, this paper reviews a range of studies that have estimated recoverable volumes of natural gas. This includes both deterministic (e.g. a single point estimates of natural gas) and stochastic (e.g. probabilistic estimates including ranges of uncertainty) modelling methods, and the strengths and limitations of the approaches employed. The overall conclusion is that some level of probabilistic assessment is required when estimating recoverable volumes of natural gas and the cost range of extraction, particularly given the huge uncertainties inherent in the development of these resources (techno-economic, geological, environmental).

A key contribution of this review, in Part 2, is how natural gas is represented in energy system and integrated assessment models. This represents how gas supply and demand dynamics are also driven by wider developments in energy and environmental systems. Standalone natural gas models, described in Part 3, include gas market complexities. These have more disaggregated time-slices/temporal horizons in order to capture seasonality and the interaction between market agents. However, there is a trade-off between the temporal disaggregation, and the overall scope of the model. In short, the decision to take gas consumption from TIAM-UCL yields the benefit of a whole systems approach in the long-run, whilst limiting seasonal disaggregation in the short-term.

Introducing a new bottom-up global natural gas production and trade model

In section III, the paper introduces a new natural gas production and trade model, which is linked to TIAM-UCL. This linkage includes an aggregation of supply cost curves from a field-level gas volume and cost database, into the regions in TIAM-UCL. The gas model is able to account for aspects of gas markets which TIAM-UCL does not have in its architecture; e.g. fiscal regimes, take-or-pay contracts, price indexation.

Given the proprietary nature of cost data for natural gas extraction, a linear regression model was used to assign supply costs (the capital and operating expenditures required to get the gas out of the ground) to gas fields where no public information was available. This gas model aims to provide insights by quantifying various parameters which determine supply costs for individual natural gas fields, both developed and undeveloped; these include water depths, reservoir depths, the levels of hydrogen sulphide or carbon dioxide, and assumed risks to investment (e.g. due to location, political conditions, etc.).

The combination of the two models is intended to model scenarios, providing new insights into future natural gas price formation mechanics and longer-term policy developments which could alter/influence supply and demand.

Publication Year:

2018

Publisher:

UKERC

DOI:

No DOI minted

Author(s):

Welsby, D.