Jintan: Optimizing Field Development through Multi-Scenario Modelling

Abstract

A Multi-Scenario Modelling approach was used to select a field development option that is robust for most if not all of the sub-surface realisations. This approach was adopted for the Jintan carbonate gas field, located offshore Sarawak, Malaysia in the South-China Sea. The Jintan field will be developed as a satellite to the nearby M1 offshore gas production facility for the supply of gas to the MLNG Tiga plant, which is currently under construction. First gas is in July 2003. Significant value has been added through an integrated, multi-discipline sub-surface study which provides the basis for the field development plan:cost savings of more than 60% were realised through integration of Jintan in Shell's existing gas infrastructure of Sarawak and by reducing the number of wells from 12 to 6,the expectation initially-in-place volumes increased by approximately 1 Tscf to 4 Tscf of gas. Key uncertainties for the Jintan field are gross rock volume (GRV) and porosity, permeability (tight streaks, karst and fault features), the initial pressure (due to possible depletion from the nearby producing fields) and the effective aquifer strength. The applied workflow to model these uncertainties included structural uncertainty modelling, stochastic inversion, static reservoir modelling and dynamic simulation. The deliverable of the (Jason) stochastic inversion study were 93 combined depth/porosity models. Five of the 93 depth/ porosity models were selected for the final porosity modelling and exported to the 3D static reservoir simulation software. The static modelling resulted in 11 different static models, which were used for the dynamic modelling. In the dynamic simulator, the initial pressure uncertainties were combined with the 11 static models into 33 sub-surface realisations which were then combined with different aquifer strengths and karst realisations. For each of the realisations an optimal development option was selected based on the currently expected capacity/offtake requirements and possible future changes/flexibility. The resulting field development plan is robust for the following reasons:The selected development option caters for the uncertainty range of almost all modelled subsurface scenarios.Dependent on the subsurface scenario, 4 to 7 wells will be drilled. The exact number of wells required to provide a given capacity will be determined during the drilling phase.Well head compression is required in almost all scenarios but never within the first years therefore allowing sufficient time to monitor the reservoir performance and, if required, execute a compression project. The described workflow, with a high level of integration between the subsurface disciplines, demonstrates the value of multi-scenario subsurface uncertainty modelling in situations where large uncertainties exist. Introduction The objective of a field development plan is to cover the key (subsurface) uncertainties to optimize the economic value of an asset. In the early phase of a field's life, only limited information about the reservoir is available and consequently the potential impact of numerous uncertainties on a field development needs to be evaluated. The aim is to design a flexible development plan which is robust to potential downsides but can also rapidly respond to upside opportunities (Ref. 1). The Jintan field development plan (FDP) provides a case study where multi-scenario modelling has been used to address uncertainties in the static reservoir model as well as in the dynamic simulation model. Scenario modelling for field development planning is widely being used in the Shell Group, and it is being recognised inside and outside Shell, as a method that allows for improved, more flexible and proactive field development planning (Ref. 2). The approach is based on creating different independent subsurface realisations that are driven by the key subsurface uncertainties that could impact on the field development, i.e. on the well locations, surface facilities design, total supply forecast etc.