The PhD thesis title is: Designing an Integrated Model for Oil and Gas  Supply Chain Conditions from Exploration to Production under uncertainty. the abstract of this thesis is:
"The aim of this study is to model and solve the problem integrated oil and gas supply chain management (SCM) by considering the Enhanced Oil Recovery (EOR) methods in upstream and midstream sectors. Although EOR has already been investigated only in upstream in the previous literature, the present study focuses on EOR in the integrated supply chain model. For this purpose, the problem is modeled as a bi-objective mixed integer nonlinear program and was solved by using BARON solver in the GAMS software. LP-metric method is used to solve the problem. To test the efficiency of the solution method, 15 problems were solved with a wide range of dimensions. Based on the results, small-size problems can be solved in less than 100 seconds to reach a relative gap of 0.01. In fact, the solution time increases rapidly when the size of the problem increases. Hence, we used three evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGAII), Multi-Objective Whale Optimization Algorithm (MOWOA), and Multi-Objective Cuckoo Search Algorithm (MOCSA) to solve the problems of large size. The results show that the evolutionary algorithms (especially MOWOA) can solve the large problems in a reasonable time. In addition, MOWOA is superior to other implemented algorithms in terms of multi-objective solution quality measures."

give a discussion to be used in my fifth chapter (discussion and conclusion chapter) in full details. use the following texts along with other publications:
With the advent of globalization and industrialization, the demand for energy has increased exponentially. It is predictable that if
global energy consumption continues, world energy consumption
will increase by 50% before 2030 [1]. Fossil fuels which include coal,
oil, and natural gas account for more than 85% of world's primary
energy [2]. Natural gas is a green fossil fuel that pollutes environment less than the others [3,4]. As environmental issues become
increasingly important, natural gas can be considered a driving
force for development and a major source of energy for the world.
To the extent that the assessments made in 2016 show that natural
gas singly accounts for about a quarter of the world's energy needs
[5]. In addition, global demand in 2017 for natural gas increased by
3.2% compared to 2016, rising up to 3757 billion cubic meters and
continuing the years of consecutive increase from 2009 [6]. With
the most robust growth in demand expected among the developing
countries, natural gas consumption is forecasted to be doubled
between 2020 and 2040 [7]. Natural gas industry is composed of
supply, production, transmission, storage and distribution sectors,
which form a large and complex supply chain. The natural gas
supply chain requires a lot of investment in all sectors. Therefore,
accurate planning on this supply chain is needed to meet future
demand. Based on definition of Christopher [8], a supply chain is a
network of organizations that are involved through upstream and
downstream linkages in the different processes and activities that
produce value in the form of products and services in the hands of
the ultimate customer.
Given the importance of natural gas supply chain, there are a
few studies in this scope. In addition, some studies have focused on
one part of the chain. Contesse et al. [9] proposed a mixed integer
programming model for Chilean gas supply chain including three
levels of producers and transmission and local distribution companies. A linear programming model for the natural gas pipeline
network of Brazilian was developed by Vasconcelos et al. [10].
Mikolajkova et al. [11] studied an optimization model for pipeline
network of gas distribution considering gas supply. Kabirian and
Hemmati [3] developed a non-linear optimization model for location and installation schedule of physical components of a gas
network including pipelines and compressor stations as a conceptual design. Conceptual design approaches are usually sub
optimal and work for short-run development planning. Andre et al.
[12] developed a non-linear programming model for capacity
expansion planning of gas transportation networks and solved this model based on a two phases approach. Uster and Dilaveroglu [13]
considered a mixed integer non-linear programming model for
designing a new natural gas transmission network or expanding an
existing network. Additionally, few studies have considered all
sectors of the supply chain, and in these studies, only gas flow
between facilities is optimized. Hamedi et al. [14] studied a mixed
integer non-linear programming model for optimizing gas flow in
natural gas supply chain and solved it by using a hierarchical algorithm. Moreover, a number of researchers have developed the
shale gas supply chain. Chebeir et al. [15] proposed a two stages
stochastic programming model to optimize the shale gas supply
chain network. Gao and You [16] and Cafaro and Grossmann [17]
formulated a mixed integer non-linear programming model for
designing shale gas supply chain. 
The natural gas supply chain has several sectors including
supply, production, transmission, storage, and distribution. These
segments decide on their own tasks individually, leading to nonoptimal decisions for the entire chain. In this paper, the structure
and processes of the natural gas supply chain (NGSC) were identified. Accordingly, extracted gas from the fields is transferred to
refineries and from refineries to DCs and other refineries through
pipelines. Also, gas is sent from import terminals to DCs. DCs
consume the needed gas and transmit the rest of the gas to other
DCs and export terminals. Also, injection into oil wells and gas
storage process in USRs are carried out by DCs. The NGSC determines the optimal location of fields and refineries along with
their capacity. Also, the capacity expansion of existing facilities
(fields, refineries, and USRs) is optimized. The NGSC determines
number of pipelines with various capacities to expand existing
pipeline routes and install new pipelines in potential routes. Also,
the rates of field extraction and refinery production, as well as the
injection and extraction rates related to USRs, gas storage amount,
gas flow through pipelines, and volume of exports and imports are
determined. The proposed NGSC has been formulated as an MILP
model to minimize the costs of capital, operating, transmission,
storage, purchasing gas from other countries, and export fine. The
global optimal solution of the model is obtained using data
extracted of Iran's NGSC at a finite time horizon of 10 years. The
model was validated by the sensitivity analysis of facility investment costs, pipeline investment costs, operating costs, transmission costs, and demand volume. The results showed that the
change in the costs of pipeline investment and transmission have
an insignificant effect on the objective function value, but the
change in the demand volume and the operating costs leads to a
remarkable change in the objective function. Additionally, variation
in the volume of demand has the greatest impact on the facility
investment costs. Finally, the problem was analyzed based on three
real scenarios. In scenario A, only joint fields can be expanded and
zero import capacity was defined as scenario B. Also, both scenario
A and B were considered as scenario C. If the model is limited to the
expansion of joint fields like scenario A, the supply chain costs will
increase. There is also a significant increase in the total supply chain
costs if joint fields are developed and import capacity was zero. In
addition, the analysis of the case study and scenarios costs indicates
that the operating expenditures have the largest share of the costs
in the NGSC. Therefore, these results draw the attention of decision
makers to the importance of the demand volume and the operating
expenditures. For the future researches, uncertainty in parameters
such as demand can be considered.