WHITEPAPER MAROS. Optimising logistics operations for Water disposal SAFER, SMARTER, GREENER

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1 WHITEPAPER MAROS Optimising logistics operations for Water disposal SAFER, SMARTER, GREENER

2 SAFEGUARDING LIFE, PROPERTY Date: September 2014 Prepared by DNV AND GL - Software THE ENVIRONMENT Copyright DNV GL AS All rights reserved. No use of the material is allowed without the prior written consent of DNV GL AS.

3 Table of contents 1 INTRODUCTION BACKGROUND FRACKING PROCESS Methodology Simulation technique 4 4 CASE STUDY Block Flow Diagram Flow profile Reliability Block Diagrams and Reliability data Maintenance strategy Linepacking operations Logistics operations 10 5 RESULTS SENSITIVITY ANALYSIS CONCLUSION ABOUT THE AUTHORS REFERENCES WHITEPAPER MAROS Page i

4 1 INTRODUCTION Unconventional gas reserves offer a new opportunity to ensure energy supply for many countries as the consumption grows and the production rates are stagnated. The subject of unconventional gas is somewhat controversial as issues related to safety and environmental impacts have not been completely explored. However, as any new technology, operators are continuously looking into new tools and methodologies to reduce the environmental impact, ensure safety and maximize performance. To appreciate how unconventional gas is changing the world of energy supply, the United States has been converting a number of LNG regasification terminals, used for import of LNG, to LNG liquefaction terminals aimed at exporting gas. Another important point is that one third of the United Kingdom s energy demand is met by gas. For instance, in 2012, around 25% of the gas used in the UK was used to produce electricity, 20% by industry and around 40% by householders. 1 Similarly to many countries, the UK government is trying to reduce carbon emissions by reducing the electricity generation by burning coal. Therefore, gas supply plays an essential role to fill this gap combined with other energy sources such as renewable and nuclear electricity. This paper aims at understanding the impact of logistics operations related to water utilization of unconventional gas production systems. 2 BACKGROUND A number of shale gas reserves are being discovered all around the world. Shale gas is fundamentally gas trapped in impermeable shale rock. This differs from traditional gas production where typically gas is contained in permeable rocks, such as sandstone. In order to produce from these reserves, operators have to enlarge or create fractures in the rock by hydraulic fracturing (often termed fracking ) which enables shale gas to flow out of the wells. Hydraulic fracturing has been playing an important part in the development of North America's natural gas resources for nearly 60 years. In the United States, an estimate of 35,000 wells are processed with the hydraulic fracturing method and over one million wells have been drilled using hydraulically fractured method since the first well in the late 1940s. Furthermore, studies have shown that up to 80% of natural gas wells drilled in the next decade will require hydraulic fracturing to properly complete well setup. Horizontal drilling is a key component in the hydraulic fracturing process. 2 With the extensive potential utilization of fracking as means to extract gas in the United States, the Environmental Protection Agency (EPA) has performed a number of analyses to understand the feasibility of hydraulic fracking. Special focus has been given to water cycle since the process is likely to use large volumes of clean water. 3 FRACKING PROCESS The Process of Hydraulic Fracturing explained by EPA is shown below: Hydraulic fracturing produces fractures in the rock formation that stimulate the flow of natural gas or oil, increasing the volumes that can be recovered. Wells may be drilled vertically hundreds to thousands of 1 Digest of UK Energy Statistics WHITEPAPER MAROS Page 1

5 feet below the land surface and may include horizontal or directional sections extending thousands of feet. Fractures are created by pumping large quantities of fluids at high pressure down a wellbore and into the target rock formation. Hydraulic fracturing fluid commonly consists of water and chemical additives that open and enlarge fractures within the rock formation. These fractures can extend several hundred feet away from the wellbore. The proppants - sand, ceramic pellets or other small incompressible particles - hold open the newly created fractures. Once the injection process is completed, the internal pressure of the rock formation causes fluid to return to the surface through the wellbore. This fluid is known as both "flow back" and "produced water" and may contain the injected chemicals plus naturally occurring materials such as brines, metals, radionuclides, and hydrocarbons. The flow back and produced water is typically stored on site in tanks or pits before treatment, disposal or recycling. In many cases, it is injected underground for disposal. In areas where that is not an option, it may be treated and reused or processed by a wastewater treatment facility and then discharged to surface water. Hydraulic fracturing is a technique used in "unconventional" gas production. "Unconventional" reservoirs can cost-effectively produce gas only by using a special stimulation technique, like hydraulic fracturing, or other special recovery process and technology. This is often because the gas is highly dispersed in the rock, rather than occurring in a concentrated underground location. Extracting unconventional gas is relatively new. Coal bed methane production began in the 1980s; shale gas extraction is even more recent. The main enabling technologies, hydraulic fracturing and horizontal drilling, have opened up new areas for oil and gas development, with particular focus on natural gas reservoirs such as shale, coal bed and tight sands. Shale Gas Extraction Shale rock formations have become an important source of natural gas in the United States. Shale gas is present in many locations in the contiguous United States, including some areas where oil or gas production has never occurred before. 3 Figure 1: Water cycle for fracturing processes 3 WHITEPAPER MAROS Page 2

6 Therefore, one of the key factors to ensure high-efficiency when producing from shale gas reserves is to guarantee the availability of water at production sites and the ability to dispose wastewater. 3.1 Methodology Reliability, Availability and Maintainability (RAM) analysis is a methodology used to predict asset performance based on two main aspects, reliability and maintainability. As with many other branches of modern engineering, system performance analysis is probabilistic as opposed to deterministic in nature. The mathematical concepts used to quantify the uncertainties of component reliability, maintenance times, and their analogies are those of statistical probability. Reliability, maintainability and availability analysis deals with the manipulation of these quantifiable uncertainties. RAM analysis can be combined to flow modelling. With this extended approach, a new set of variables can then be tracked when analyzing the performance of the system. One of these variables is the impact to production rates during an event (failures, planned maintenance, operational bottlenecks): Example of time-based results without flow modelling: When combining flow modelling to RAM analysis, the impact is now based on the flow rate (or production rate) available at the time of the failure and the system capacity. For example, if a pump has a failure that reduces the system capacity on 20% and the flow available at the time of failure is 100% of the pump s capacity, the impact is 20% of production loss throughout the repair. However, if the same pump is operating at a reduced production rate, the impact will be different. Assuming the same pump is only required to export with 80% of its capacity, in this case, the aforementioned failure will not impact production. This reduction on the production rate commonly seen for oil and gas production fields since the reserves of these products are finite. Example of production rates-based results with flow modelling: WHITEPAPER MAROS Page 3

7 In addition to a more extensive and precise way of computing losses, the ability to integrate flow modelling to the analysis gives the analyst features to account for typical challenges related to logistics such as number of cargos, travel delays, berthing processes, trucks available and more. 3.2 Simulation technique RAM analysis is typically combined to simulation techniques. An event-driven algorithm can be used to create life-cycle scenarios of the system under investigation accounting for its reliability, maintainability and operating policies. A life-cycle scenario is a chronological sequence of events which typify the behavior of a system in real-time. The methodology can create an infinite number of such scenarios for any given system, each one being unique, however sharing the commonality of being a feasible representation of how the system would behave in practice. By analyzing groups of life-cycle scenarios, statistics can be extracted relating to the system's performance. Figure 2 below illustrates two life-cycle scenarios for a specific system and the resultant statistic derived from the outcome of 100 life-cycles. WHITEPAPER MAROS Page 4

8 Figure 2: Performance characteristics life-cycle scenarios The simulation technique applied is based upon event generation and is commonly referred to as a 'direct simulation' method. The virtual model formed from the model data moves from one distinct state to another, governed by the occurrence of a sequence of events. The state of the model at any point in time (simulated time) is represented by a set of variables, as each new event occurs one or more of the variables representing the model changes. Progress of the simulation is in steps, from the occurrence of one event to the occurrence of the next until the simulated time exceeds the specified design life of the system being modelled. Life-cycle scenarios are built-up by continuously monitoring the set of variables which describe the state of the system model during a simulation. It is implicit, therefore, that the 'degree of reality' within the simulation depends entirely on the sequence of events generated. Event generation itself is a straightforward procedure in computing terms involving random number generation, distribution sampling etc. The key to success of a simulator lies in its ability to manage event sequences and carry out appropriate actions in accordance with these sequences. Finally, a good representation of the system will rely a lot on the methodology/tool used to model all these different variables. 4 CASE STUDY This case study describes the design process of a shale gas production unit. This system is assumed to operational only for 5 years which simulates the short duration of a shale gas reserve. 4.1 Block Flow Diagram A number of elements described at Section 3 can be seen in the Block Flow Diagram. The Block Flow Diagram (or flow network) is a logical connection between the different elements of a production system. WHITEPAPER MAROS Page 5

9 Figure 3: flow network The interaction of the different elements in the flow network can be explained as follows: - There are 6 wells feeding the system - A mixture of gas and water defined for each Well production node - Once the production reaches the Gas treatment plant, streams are separated into Water and Gas. The Gas stream feeds the Compressor station node and Water is sent to the Wastewater treatment plant. - Gas is compressed and exported through a pipeline. The Gas pipeline can be temporarily stored gas in the pipeline system itself, through a process called line packing. - Water is treated and exported through trucks to be disposed. A buffer tank is placed just after the Wastewater treatment plant node so the export operation depends on the availability of trucks. This case study analyses challenges related to water export operations. However, this could be easily extended to account for import scenarios. - The Water Disposal Storage has a maximum volume of 160 bbls. 4.2 Flow profile Another application, DNV GL s Synergi gas has been used to estimate the gas production profile which is shown in Figure 5: WHITEPAPER MAROS Page 6

10 Figure 4: flow profile for the gas production system The potential gas production can be calculated using a few considerations in Synergi gas. For instance, Arps 4 equation can be used to calculate the declining profile based on extrapolating trends in the production data from oil and gas wells. Another option is to define the initial flow (for example, obtained from a flow test) and apply a general field decline rate that Synergi gas will use to generate the flow points. This information is then plugged directly into the RAM model to account for the variability of gas production throughout the system life. Figure 5: Production profile This also gives what is the potential production from the field which will be used at the bottom part of the production efficiency equation: 4 J.J. Arps was an American geologist who published a mathematical relationship for the rate at which oil production from a single well decline over time (1945). His paper made several references to existing methods and theory about decline analysis. References included Arnold and Anderson (1908), W.W. Cutler (1924), H.N. Marsh (1928), and R. E. Allen (1931). Many contemporary published papers have tried to investigate or modify the Arps decline based on theoretical derivations. However, after 70 years, the original method is still widely in use. WHITEPAPER MAROS Page 7

11 Production efficiency is one of the main outcomes of a RAM analysis and it relates to total volume produced to that which would have produced had all equipment run without failures throughout the system life. The water production profile must also be defined in order to correctly design the capacity of the water production systems. The expected produced water depends on the formation but volumes range from 200 to 1,000 gallons per million cubic feet of gas produced. Typically, a rate of 2 to 10 barrels (84 to 420 gallons) per day. 5 Figure 6: water production profile 4.3 Reliability Block Diagrams and Reliability data Under each element of the Block Flow Diagram, a Reliability Block Diagram is defined. RBDs are a logical representation of the system connection, taking into account the path of success of the system mission, in this case flow. If you have items in series, when one of them is in a failed state there is no way for the system to move forward. However, if you have items in parallel, it means that there is more than one success path in the system. The RBD for the compression system is shown in Figure 7: Figure 7: RBDs for the compression system 5 Notice of Final 2010 Effluent Guidelines Program Plan, 76 Fed. Reg. 66,286, 66, (October 26, 2011); James M. Silva et al., Produced Water Pre-treatment for Water Recovery and Salt Production, January 26, 2012, iii, WHITEPAPER MAROS Page 8

12 Each one of the blocks in a reliability block diagram represents one event that can lead to production loss. In the specific case of this model, each one of the blocks represents an equipment item. Below the equipment level, the user must define failure modes failure modes are different ways in which the equipment can fail. The appropriate reliability data was added to the model to reflect this scenario. Equipment Table 1: Reliability data for the compression system MTTF Failure distribution Repair distribution (years) Compressor Exponential 0.69 Compressor Suction Drum Compressor Suction Cooler Exponential 4.96 Exponential 13.3 Condensate Pump Exponential 5.56 Constant Repair Time Constant Repair Time Constant Repair Time Constant Repair Time MTTR (hours) Compressor Driver Exponential 4.01 Constant Repair Time Maintenance strategy Maintenance strategy modelling normally includes an extensive assessment over the maintenance resources such as spare parts, crews and safety accessories. The repair tasks can be prioritized: critical failures will be repaired immediately whereas incipient failures will be repaired on an opportunistic basis meaning only when a critical failure (causing complete shutdown) occurs in a particular location, the incipient failure is then repaired (causing no shut down). This model assumes that a maintenance crew is required to repair any failure in the system. Critical failures are prioritized over incipient failures. Incipient failures are not repair until a Critical failure occurs at the same location. Failures at any compressor will require a spare part from at the warehouse. There are 4 spares available and it takes 20 days to restock once the numbers of spare available reach the level of Linepacking operations Figure 8: Locations The ability to compress natural gas enables operators to pack (i.e. store) gas into the pipeline to compensate for fluctuations of gas demand or failures. This model includes linepacking operation aiming at reducing the impact of failures upstream to the gas pipeline to the delivery of gas. Additionally, an event at the customer has been defined to model any interruption. On the occurrence of this event, the compression station will continue to pump gas into the pipeline until the pipeline is unable to take more gas, reaching its maximum safety internal pressure. WHITEPAPER MAROS Page 9

13 Figure 9: Gas branch with gas stored at the gas pipeline The system capacity is reduced to 70% during the first 12 hours to build the pipeline. 4.6 Logistics operations The logistics operations are modelled to analyses the ability of the system to export the water produced by the fracking system. The logistics can be separated into three main parts: export system, logistics operations and import system. Each stage is described below: Export system Figure 10: Block flow diagram for the Export system Both the Water Disposal Storage and the Truck parking have the export capacity of 200 bbls/day. The Waste water treatment plant has a capacity of 40 bbls/day and feeds the Water Disposal Storage node. The Water storage tank has a maximum volume of 200 bbls so in there is any interruption downstream to the storage tank, the Gas treatment plant can operate for up to 5 days. Logistics operations Figure 11: Block flow diagram for the logistics operations Tank trucks are described by their size or volume capacity. Large trucks typically have capacities ranging from 165 bbls to 375 bbls. These trucks typically have a cylindrical tank upon the vehicle lying horizontally. Additionally, tanks will almost always contain multiple compartments or baffles to prevent load movement destabilizing the vehicle. There are three Water Road Cars available. Each water road car has the capacity of carrying 250 bbls of water. Figure 12: Number of trucks available WHITEPAPER MAROS Page 10

14 The duration of the process to fill-up the truck can be calculated dividing the truck capacity by the normal export capacity of the Truck parking node. So results of 1.25 of day which is 30 hours. The export operation is considered to occur only during daylight as shown in the window below: Figure 13: Definition of daylight operations Additionally, the duration of the hose connection operation is defined as 30 minutes to connect and 30 minutes to disconnect. Import system Figure 14: Block flow diagram for the Import system Both the Water Disposal and the Truck parking have the export capacity of 200 bbls/day. 5 RESULTS The virtual model for the fracking unit simulates 5000 cycles. This means the application is sampling events for 5000 different lives of 1 year. With this information, the analyst can create a graph that shows how the Monte Carlo method averages to single value after running a number of cycles. WHITEPAPER MAROS Page 11

15 Figure 15: Application generating a running efficiency curve The calculated production efficiency for the system is %. This production efficiency is the averaged throughout the all the simulated lifecycles. A graph showing the distribution of the different production efficiency throughout the different lifecycles can be generated: Figure 16: normal distribution A graph over describing the volumetric production of each stream can be generated: WHITEPAPER MAROS Page 12

16 Figure 17: Produced volume per export stream The criticality graph shows what is the production loss associated to each event defined at the virtual model. This is then ranked to show what events are the biggest contributors to the production loss: Figure 18: criticality at the gas customer node For this model, the biggest contributors for the production loss are listed below: - The Water production system is responsible for % of the losses - The Chemical injection is responsible for % of the relative losses - The Pumping is responsible for % of the losses The simulation process keeps track of level within the tanks and the analysis generates two graphs: - Probability of non-exceedance for the tank level - Level within the tank throughout a simulated lifecycle The probability of non-exceedance graph for the Water disposal storage tank indicates an extremely low probability of the tank level exceeding 74% throughout the life of the system. WHITEPAPER MAROS Page 13

17 Figure 19: Probability of non-exceedance for Water Disposal Storage tank level Armed with this information, the analysis of the storage tank can be extended to understand the behavior of the tank for an individual life-cycle. The graph below shows the level status of the Water Disposal Storage tank for the first life-cycle. From this graph, it is possible to conclude that, the tank level never goes beyond the 85% and, the maximum level achieved for the first cycle is 120 bbls which represents 60% of the tank s capacity. This conclusion is aligned with the answer given by the first graph. Figure 20: simulated level for Water Disposal Storage tank for cycle 1 The simulation details the duration of each operating mode of the Water trucks. The average duration of each operation is shown below: WHITEPAPER MAROS Page 14

18 Figure 21: Operations statistics graph for the Water truck One parameter is dominant on this graph: Queue to Load (days. This is the total average time (in days) spent by the resource queuing to load a cargo. 6 SENSITIVITY ANALYSIS RAM analysis empowers the analyst to perform a number of changes to the system in order to optimize design configuration, maintenance strategy and operational procedures. With the current design configuration, number of trucks available for the transport of water and maintenance strategy, the level of the Water Disposal Storage tank is unlikely to go beyond the 35% of its capacity. Therefore, the following sensitivities are suggested: - Reducing the size of the storage tank to 150 bbls - Reducing the size of the storage tank to 125 bbls - Reducing the size of the storage tank to 100 bbls - Reducing the size of the storage tank to 75 bbls - Reducing the size of the storage tank to 50 bbls This change impacts directly on the capital expenditure. Different tank sizes can be tested in order to provide an optimum balance between capital expenditure and risk of top-out interrupting upstream production. The production efficiency of each case is detailed below: Production efficiency (%) Standard deviation (%) Base case Reduced storage size (150 bbls) Reduced storage size (125 bbls) Reduced storage size (100 bbls) Reduced storage size (75 bbls) Reduced storage size (50 bbls) WHITEPAPER MAROS Page 15

19 The estimated production efficiency is exactly the same for all the sensitivities except the case with 50 bbls. This means that the base case has over specified the size of the storage tank and the performance parameter is only influenced when the tank volume is reduced to 50 bbls indicating the minimum. The probability of non-exceedance graph for the base case indicates an extremely low probability of the tank level exceeding 35% throughout the life of the system. Figure 22: Probability of non-exceedance for Water Disposal Storage tank level Combining all curves from the sensitivities, we can see the curve being slightly shifted to the right every time the tank size is reducing. This indicates that the risk of topping out increases and the tank volume is reduced: 1.00% 0.95% 0.90% 150 bbls 125 bbls 100 bbls 75 bbls 50 bbls 0.85% Figure 23: Probability of non-exceedance for all sensitivities combined Another interesting outcome is shown at the level status per life-cycle. The graph for the Water Disposal Storage tank for the first cycle shows the blue line (maximum volume) becoming closer to the green bar (maximum simulated volume at a certain period of time). This shows an increased risk of topping out however no top-out is registered for the different sensitivity cases. WHITEPAPER MAROS Page 16

20 Tank volume Level of the tank 125 bbls 75 bbls 50 bbls Figure 24: simulated level for Water Disposal Storage tank for cycle 1 A more thorough analysis could then be performed in order to check: - Availability of the maintenance resources in that specific geographical region - Reliability and maintainability of the trucks - Transient cost daily rates - Smaller - Availability of specialized water injection pump engineers, etc. This would then feed back into the analysis supporting the decision on how many trucks should be contracted with a number of defined criteria on cost and revenue. 7 CONCLUSION RAM analysis plays a key role when analyzing optimal operational strategies in the oil and gas industry. Informed decisions can be made and uncertainty in regards to the production behavior can be accurately predicted and therefore avoided or reduced. By running the base case, the analyst builds a deep understanding on how the system may behave throughout its life. WHITEPAPER MAROS Page 17

21 In order to save capital expenditure, a number of scenarios testing the reduction of the storage tank size are performed. This empowers the analyst to find an optimum balance between capital expenditure and risk of top-out the storage tank (which interrupts upstream production). Below is a graph of the graph probability of non-exceedance based on the tank level for all the cases: 1.00% 0.95% 0.90% 0.85% Level 150 bbls 125 bbls 100 bbls 75 bbls 50 bbls 8 ABOUT THE AUTHORS Victor Borges, RAM Product Manager at DNV GL, is a chemical engineer with experience performing risk and reliability analysis for assets in the oil and gas industry. He is responsible for DNV GL s world-leading simulation software packages Maros and Taro. Nick Gorzynski, Regional Sales Manager at DNV GL, is a chemical engineer with experience as a field engineer performing Well Cementing Services and Well Stimulation Services in the Permian basin. He also has experience as a project engineer using Synergi Gas and Synergi Water to model gas gathering operations. 9 REFERENCES /1/ DNV GL Software. (2014, 05 13). Maros. Retrieved 2014, 05 13, from /2/ /3/ America s New Energy Future: The Unconventional Oil and Gas Revolution and the US Economy, Volume 1: National Economic Contributions. October /4/ IHS Global Insight, Measuring the Economic and Energy Impacts of Proposals to Regulate Hydraulic Fracturing, /5/ America s New Energy Future: The Unconventional Oil and Gas Revolution and the US Economy, Volume 3: The Manufacturing Renaissance. October /6/ American Chemistry Council, Shale Gas and New Petrochemicals Investment: Benefits for the Economy, Jobs, and U.S. Manufacturing, March /7/ America s New Energy Future: The Unconventional Oil and Gas Revolution and the US Economy, Volume 3: The Manufacturing Renaissance. October WHITEPAPER MAROS Page 18

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