A Literature Review of Hybrid System Dynamics and Agent-Based Modeling in a Produced Water Management Context
Abstract
:1. Introduction
2. Prior Work
3. Contextual Background
4. Why a Hybrid Modeling Approach?
- Explaining how relationships emerge and evolve among agents (e.g., the geospatial distribution of disposal or injection wells).
- Explaining how these relationships affect the state of the system (total cost, oil production, water levels, and so on, as influenced by the dynamics of the geospatial distribution of oil and water wells).
- Explaining how the state of the system affects the relationships (e.g., the distribution of oil and water wells as influenced by oil production and groundwater levels).
5. Conceptual Framework
- (a)
- Being dynamic: Dynamic complexity emerges from the interactions among the agents over time [32]. In our framework, the key variables of the system such as oil production, water use for hydraulic fracturing, produced water used in secondary recovery and reservoir pressure maintenance, and decisions for treatment and disposal, all interact with each other and all change over time.
- (b)
- Being spatial: It relies heavily on spatial information and data, as managers must make decisions on where to drill a new oil well (where the produced water is generated), where to dispose of the produced water, and where to inject treated produced water.
- (c)
- Being heterogeneous: Agents representing stakeholders, wells, or well owners act differently based on their different input, analysis, and interests; this heterogeneity also adds to the complexity of the problem.
- Individual Companies: At this level, there could potentially be two types of agents (Figure 3). First, there are oil companies that impact the system by making decisions such as where to drill new wells and how to deal with the produced water at each location. Second, there are oil wells with different specifications regarding the volume of produced water and associated geological formations. The main attribute that differentiates oil companies as different groups of agents in our model is their size. According to our interviews with research and industry experts, major oil companies are socially driven to explore options for using less freshwater, to treat and reuse produced water, and to reduce impacts to the environment. Compared to large players in the system, independent oil companies usually have more immediate considerations and fewer resources for long-term investments such as large-scale produced water treatment. At this level, although many factors are considered for PWM, cost is the ultimate driving force behind management decisions, followed by the need to maintain a positive public perception. Specifically, when the profit margin of production drops and remains below a certain threshold for long enough, production ceases and the well is closed [26]. On the other hand, for agents representing the wells, geospatial attributes such as their distance from proper disposal areas and the transportation cost of the produced water will drive the management decisions.
- Local Community: The impact of produced water management decisions can be seen mostly at the local level where resulting pollution directly impacts the environment (Figure 3). For example, some produced water may spill during transportation, or it may be partly responsible for an increase in the seismic activity in nearby areas; such examples may impose significant challenges to the local population. One of the main drivers of the changes in regulations regarding produced water is the public pressure on regulatory institutions. Each new regulation requires the oil companies to modify their decisions toward better environmental outcomes. These decisions change other spatial variables such as quantity and quality of available water, seismicity risks, environmental pollution, and so on. These changes drive economic and system-level changes such as the water budget, environmental regulations and policies, and carry societal costs. These factors then feed back into the decision functions of produced water managers as informational inputs for their cost-benefit analyses.
- Aggregate region: Treatment of the produced water can potentially reduce the amount of water available for other activities such as agriculture or industry (Figure 3). For example, based on the quality of the treated produced water, it can be reused for fracking. This process could reduce the need for freshwater, and therefore, reduce the extraction of water from almost all non-renewable groundwater aquifers in the region. Because of the level of aggregation, more research is needed in order to connect the cause and effect processes. This goal can only be achieved by using integrated tools such as hybrid modeling.
6. The Systematic Review Method
7. A Hybrid Dynamic Simulation for Produced Water Management
- An example in the literature: Schieritz and Größler [52] provide an example of this kind that can be captured only partially by Swinerd and McNaught’s subclass 1. Their hybrid model addresses a supply chain management issue. The model’s agents (companies within the supply chain) have two system dynamics modules: ordering and evaluation. The AB model is written in eM-Plant while the SD modules are developed in Vensim. A third module stores and processes the input and output data of the system dynamics modules and regulates the communication between Vensim and eM-Plant via Dynamic Data Exchange (DDE), which is a communication system. In this model, eM-Plant connects to Vensim (the DDE server), and the input and output data and commands are transferred via the established channel.
- An example in PWM: A SD module of oil production in a SD platform such as Stella or Vensim, and an AB module of spatial dynamics of produced water injection wells in an ABM platform such as NetLogo communicate through an external protocol that runs through input/output spreadsheets. The SD oil production module simulates the volume of produced water over time. This simulation output will be exported to the SD output spreadsheet, which will serve as the AB input. The AB model reads the simulated produced water and distributes it between the injection wells based on a decision rule, e.g., a cost minimization function that determines which injection well is closer to each produced water disposal pond.
- Example in the literature: An example of this kind is Duggan’s model [42], although the model is fully developed using SD tools and called "agent-oriented SD" by the author. In this model, each player (agent) within the supply chain has a stock and flow structure. The output of these SD models then drives the behavior of the rest of the AB model.
- Example in PWM: An AB module of spatially distributed injection wells where each well, as an individual agent, contains a SD structure. The SD structure could be a stock representing the capacity of the well that is available for additional injection of produced water. The produced water could be allocated to an injection well that has the greatest remaining capacity and that is closest to the point of distribution, thereby having the minimum transportation cost.
- An example in the literature: An example of this kind is Anderson, Lewis, and Ozer [27], which investigates the dynamics of team performance in knowledge-based organization. In their model, expertise is modeled as stocks while interactions between members and diversity-based subgroups are agent-based. In general, each variable in the SD model is subscribed to work as a small AB module.
- An example in PWM: A SD oil production module connects directly to an AB module of spatially distributed injection wells. This example is similar to the Class A example with one major difference: the SD and AB modules are connected in a single environment and the interaction between these two modules occurs directly at each time step without any mediation.
- An example in the literature: Swinerd and McNaught [36] do not mention this class explicitly. However, the model they provide in their other works [46,53], reveal that they acknowledge the existence of this class which combines Classes B and C. The only other instance of this class in our review is Alfaris and others [54] that present a model for national energy planning in Saudi Arabia.
- An example in PWM: The AB module of injection wells explained in the Class B example is connected to a SD module of oil production in a feedback loop. Institutional dynamics driving regulation changes could also be modeled as a SD module. Regulations then will feed into the decision functions in the AB module and affect total costs of produced water management options. These decisions will impact the regional water budget, which could be represented by another SD module. The outputs of the water budget (e.g., water availability index), in turn, feed back into the other AB and SD modules to determine new institutional regulations and PWM decisions.
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Langarudi, S.P.; Sabie, R.P.; Bahaddin, B.; Fernald, A.G. A Literature Review of Hybrid System Dynamics and Agent-Based Modeling in a Produced Water Management Context. Modelling 2021, 2, 224-239. https://doi.org/10.3390/modelling2020012
Langarudi SP, Sabie RP, Bahaddin B, Fernald AG. A Literature Review of Hybrid System Dynamics and Agent-Based Modeling in a Produced Water Management Context. Modelling. 2021; 2(2):224-239. https://doi.org/10.3390/modelling2020012
Chicago/Turabian StyleLangarudi, Saeed P., Robert P. Sabie, Babak Bahaddin, and Alexander G. Fernald. 2021. "A Literature Review of Hybrid System Dynamics and Agent-Based Modeling in a Produced Water Management Context" Modelling 2, no. 2: 224-239. https://doi.org/10.3390/modelling2020012