Physical model

The main goal of developing physical models is to compute the mass flows at the level of each equipment item and to determine the possible energy requirements (e.g. power) for each process transformation. These models are developed in specific programming languages or simulation software, in which sets of equations (e.g. material and energy balances) are implemented and solved with the use of mathematical algorithms (e.g. Wegstein and Newton- Raphson methods) [153].

A process design approach can either consist of designing new facilities (grassroot) or of modifying existing ones (retrofit). In both cases, the overall aim is to design the processes with the appropriate physical and/or chemical transformations that are necessary to produce the desired outputs [154].

The main step is therefore to establish an inventory of the available resources (material and energy), identify the product (and possibly by-products) requirements and specifications, and investigate the possible pathways between the inputs and outputs, with respect to operating conditions that are feasible in practice and thermodynamically consistent. The different process alternatives, which are deduced from an extensive literature review and a survey of the technologies currently used, can be evaluated simultaneously and be embedded in a general block flow superstructure [155–157].

The physical model follows a sequence in three steps:

(1) pre-processing;

The model to be investigated is called, and the parameters that are required to run it are transferred or directly calculated from the input data given by the model user. For instance, when designing a new oil and gas separation process, the parameters that can be chosen are the operating pressures and temperatures of the vapour-liquid separators.

(2) simulation;

The model is solved numerically based on the pre-processed data and the selected equation solver, which can be, in this work, either Aspen Plus or Aspen Hysys (sequential) or Belsim Vali [40] (simultaneous).

(3) post-processing;

The results of interest are organised for further use in the analysis and optimisation routines. For example, data such as temperatures, heat capacities, mass and heat flows should be extracted to perform a pinch or energy integration analysis.

3.3.1 Thermochemical modelling

Chemical modelling. The reservoir fluids extracted from petroleum reservoirs contain a large variety of hydrocarbons, and complete compositional analyses are rarely conducted. The crude oils, as processed at the outlet of the offshore facility, are therefore characterised by their bulk and distillation properties, rather than by their chemical compositions. Bulk properties refer to properties measured when analysing the complete crude. Distillation properties refer to properties measured when analysing individually smaller fractions of the crude mixture [14, 17].

These fluids are thus modelled as mixtures of known and unknown, named hypothetical or pseudo-components. Light fractions, also called light ends, because they contain low-weight hydrocarbons, are represented by known components such as methane, ethane and propane. Hydrocarbons forming the heavy fractions are lumped into hypothetical components, each representing a certain number of real chemical compounds. Pseudo-properties such as the acentric factor are derived from the true boiling curve of the mixture [158].

Thermodynamic modelling. The calculations of the physical (e.g. density) and thermody- namic (e.g. internal energy) properties of each substance require information such as the

pressure, volume and temperature (PυT). These properties are predicted using chemical ther-

modynamic models, which are based on either equations of state (EOS) or activity coefficient methods. The several chemical systems encountered in oil and gas modelling, and for which different models are applied, can be grouped into:

• ideal gases (e.g. air processed through the gas turbines): the Van der Waals (VDW) [159] EOS is applied, as it is satisfactory for predicting the thermodynamic properties of gases with perfect behaviour, but not near the critical point and not for phase equilibria [160];

• pure water, in liquid, vapour and supercritical states (e.g. water in a steam cycle): the tabular properties derived by the International Association for the Properties of Water and Steam (IAPWS) [161] are used;

• hydrocarbons in vapour phase (e.g. light gases, mostly containing methane and ethane): the Redlich-Kwong with Soave modifications (SRK) [162] EOS is applied, as it predicts more accurately the vapour-liquid critical properties of light gases than the VDW EOS;

• hydrocarbons in liquid phase (e.g. gas condensate, composed of propane and butane, and oil): the Peng-Robinson (PR) [163] EOS is chosen, as it is significantly more reliable than the SRK EOS for the calculations of liquid volumes of hydrocarbons [164, 165];

• non-ideal mixtures of polar compounds (e.g. water-glycol solutions in dehydration pro- cessing): the Twu-Coon-Cunningham (TCC) [166] EOS is considered, because it is more accurate than the PR EOS for estimating the interactions between polar compounds;

• non-ideal mixtures of polar and non-polar compounds at high temperature and pressure (e.g. hydrocarbons, water and glycol in gas absorption): the Schwartzentruber-Renon (SWR) [167, 168] EOS is taken into account, as it is as accurate as activity-coefficient models for predicting the thermodynamic properties of such non-ideal solutions;

• non-ideal single- or two-liquid phase mixtures at low pressure (e.g. treated water with methanol): the Non-Random-Two-Liquid (NRTL) [169] activity model is applied, as water-methanol mixtures at normal conditions are highly non-ideal;

• non-ideal aqueous and mixed-solvent electrolyte solutions at low pressure (e.g. salts, amines and carbon dioxide): the Electrolyte Non-Random-Two-Liquid (eNRTL) [170] activity model is preferred because of the presence of electrolytes;

• non-ideal solutions at low to high pressures without ion formation but with physical absorption (e.g. methanol solvent and carbon dioxide): the Perturbed-Chain Statisti- cal Associating Fluid Theory (PC-SAFT) [171] EOS is more appropriate for modelling systems with important anisotropic association and electrostatic interactions.

3.3.2 Process modelling Data collection

The data used for the calibration and validation of the process model came from various sources, such as (i) archived data (former measurements), (ii) component data-sheets, (iii) en- gineering manuals (documentation of the anti-surge recycling), (iv) engineering assumptions (hypotheses on heat and pressure losses), (v) fiscal and online measurements (data subject to taxation or for monitoring purposes), (vi) process flow and instrumentation diagrams (plant processes and major equipments), (vii) public domain (estimations of the oil flows), and (viii) reference textbooks (general descriptions of oil and gas processing).

The evaluation of the data quality from the online measurements showed that, (i) when several sensors are placed at the same location (e.g. venting and flaring systems), a single averaged-value was stored, and no information on the averaging algorithm was available; (ii) some values were kept as constant values inside the database (e.g. volumes of flared gases at high-pressure), as long as the standard deviation of the new measurement did not exceed a certain threshold limit; (iii) it was not possible to identify if the updated values were measured and registered at the same point in time, and this generated an additional uncertainty.

The data used for the model calibration and validation consisted mainly of values received from the process database. These values were not the values directly received from the sensors, but they were values that were received after post-processing between the sensor and the database. They were given on a rate of 1/s and had an accuracy of up to 15 digits, in the case of the Draugen platform. Time intervals with stable conditions were considered, and the data were time-averaged to reduce the impact of transient conditions on the system modelling.

Most data used for the calibration of the Draugen platform, which is the core case study of this work, were received between the middle and the end of this project. They were used to adjust the preliminary values deduced from the authors’ experience and literature studies. The data related to the other case studies are available in Voldsund et al. [172, 173].

Data adjustment

The chemical compositions of the feed streams, i.e. at the inlet of the processing plant, were deduced and adjusted from the crude oil, fuel gas and water compositions and rates, as measured at the outlets. This backward approach was suggested by the platform engineers and was successfully applied in the work of Voldsund et al. [172]. A direct and forward approach may be inappropriate, as there is a lack of knowledge on the properties of each well-stream. On the contrary, the application of a backward approach is eased by the measurements of the oil, gas and produced water flows. These measurements were available for each case study, as they are made obligatory by the Norwegian Petroleum Directorate (NPD).

The same reasoning was applied at the level of each chemical compound, as a detailed compositional analysis was not available for the reservoir fluids. In the case of the platforms investigated in this work, the chemical compositions of the fuel and export or injected gas streams were available: they were measured several times in the recent years, and crude oil assays were made available by oil companies or by their industrial partners. This approach was found to be easier to apply if no information was available on the uncertainties of the measurements and on the reliability of the sensors.

Data reconciliation

The measurements on the platform inflows have significant uncertainties, because of the multiphase properties of the well-streams and the changes in the field conditions over its lifespan [174, 175]. They can help in monitoring the well performance but are generally recommended for use along with measurements on the platform outflows [176]. These ones are constrained by the the Norwegian Petroleum Directorate: limitations on the maximum uncertainties that can be allowed were set, for fiscal reasons [177, 178]. The uncertainty levels at 95 % confidence stated by the NPD [177–179] are 0.30 % for oil, 1.8 % for fuel gas, 1.0 % for sales gas and 5 % for flared gas. Values for lift gas and for vented gas were not found and were assumed to be 1.8 % and 5 %, respectively, as for fuel and flared gas. Data were therefore reconciliated when possible to improve the consistency of the models.

3.3.3 System simulation

The processing plant was simulated using Aspen Plus®_{[39], Aspen Hysys}®_{[147], and Vali}®

[40]. The power generation plant was simulated by using the in-house tool Dynamic Network Analysis (DNA), which is a program developed at the Technical University of Denmark [149].