WHITEPAPER TRANSPORT LOGISTICS - DNV

SAFER, SMARTER, GREENER

WHITEPAPER

TRANSPORT LOGISTICS

Evaluation of the effect of different logistic conditions applied to oil supply chain

Prepared by DNV GL – Software

2017

© DNV GL AS. All rights reserved

This publication or parts thereof may not be reproduced or transmitted in any form or by any means, including copying or recording, without the prior written consent of DNV GL AS

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Table of contents

1 INTRODUCTION .............................................................................................................. 1

2 SUPPLY CHAIN DEFINITION ............................................................................................. 1

2.1 Description of oil supply chain logistics 2

3 PERFORMANCE FORECASTING METHODOLOGY ................................................................... 4

4 CASE STUDY: FLOATING, PRODUCTION, STORAGE AND OFFLOADING .................................. 5

4.1 Description of digital twin 5

4.2 Maros for performance forecasting 5

5 RESULTS ..................................................................................................................... 14

6 SENSITIVITY ANALYSIS ................................................................................................. 19

6.1 Sensitivity 1: Operating rules 20

6.2 Sensitivity 2: Number of ships 22

7 CONCLUSION ............................................................................................................... 23

8 REFERENCES ................................................................................................................ 25

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1 INTRODUCTION

The petroleum industry is separated into three major operations: upstream, midstream and downstream

activities.

The upstream operation, also known as the exploration and production (E&P) sector, covers the

exploration, production and transportation of gas and crude oil from the oil fields to processing units,

where the final products are produced, mainly refineries and gas treatment facilities (GTF).

Midstream operations are commonly included as part of downstream operations for much of the oil and

gas industry. The midstream and downstream activities take place after the initial production phase and

through to the point of sale.

The downstream operation is a term commonly used to refer to the refining of crude oil and the selling

and distribution of natural gas and products derived from crude oil. Such products include liquefied

petroleum gas (LPG), liquefied natural gas (LNG) gasoline or petrol, jet fuel, diesel oil, other fuel oils,

asphalt and petroleum coke.

There is an important and common area to all operations: the transportation logistics. Basically, all

activities need to assess the level of product to be delivered. On the upstream operation, there is a need

to supply a certain amount of the oil and gas to the processing units or onshore storage terminals – a

failure during this stage of operation can directly impact the operation for the whole chain. On the

downstream operation, the transportation is even more important because the product is frequently

delivered directly to the end user.

Logistics play a big role in a company’s operation and are critical to the competitiveness of that company.

The demand for products can only be satisfied through the proper and cost-effective delivery of goods

and services.

Nowadays, logistics operations represent a big share of the market and the expenditures on these

operations are in the order of trillions of dollars annually.

Despite the recession in the United States in 2010, the logistics operations costs reached $1.2 trillion, an

increase of $114 billion from 2009. The number is even more incredible when evaluated by the

contribution on the nation’s gross domestic product (GDP). The logistics participation in the US economy

represents 8.3 per cent of the nation's gross domestic product (GDP) in 2010, compared with 7.8 per

cent in recession-wracked 2009.

In this scenario, where trillions of dollars are in place, even a small increase to the efficiency of deliveries

schedules and contracts represent a large amount of money.

2 SUPPLY CHAIN DEFINITION

The supply chain is defined as follows (Hugos, 2011):

“A supply chain consists of all stages involved, directly or indirectly, in fulfilling a customer request. The

supply chain not only includes the manufacturer and suppliers, but also transporters, warehouse,

retailers, and customers themselves.”


The main difference between the concept of the supply chain and the logistics operation is the boundary

of each activity. Logistics operations are usually related operations that occur within a company

boundary whereas the supply chain represents a network of companies.

Taking this definition, a logistics operation evaluation must consider:

Customer contracts: this represents what the customers need of the product – in this case, crude

oil and gas. Usually, suppliers have several contracts with different customers for products with

different specifications. Managing all this contracts is one of the most difficult tasks in a logistic

assessment.

Buffer level management: the objective of buffer level management is to align and maintain the

lowest levels possible that will meet the contracts agreed with the customers.

Supply: the process of building inventory to the targets established in buffer level management

planning. Make sure the levels on tanks agree with what needs to be exported.

Transportation: physically links the sources of supply chosen in sourcing with the customers.

This involves many different resources that must be aligned to optimize efficiency.

Storage: in an ideal scenario where the activities above are well implemented, the storage

activity may be outsourced. However, a failure in a critical equipment of a platform might shut

down the production for days, stopping the whole process. Storage tanks increase the availability

of a certain product when demand increases or, in a case where the transfer is delayed, the

product can be held without the need to stop the whole system. It also allows more time for the

operator to adjust the product within a certain specification or, more related to the purpose of

this article, to prepare for expected or unexpected outages.

The focus of this article will be on the evaluation of different logistics conditions and how they have an

impact on the final production efficiency of the supply chain. The focus will be the transportation of oil

between the platforms to an intermediate storage farm tank.

2.1 Description of oil supply chain logistics

A simplified oil supply chain can be explained with the following parameters:

Platforms will produce crude oil

Transportation is typically performed by crude oil tankers or pipelines that deliver production to

tank farms

Tank farms feed refineries that will process crude oil to produce more valuable fractions of oil

Refined products are distributed to consumers

The starting point of the supply chain is the production of crude oil from offshore platforms. At this stage

of the supply chain, several factors can impact the supply chain efficiency. Failure of equipment items,

planned shutdowns and operational bottlenecks will cause disruption to the supply chain.

One important component of the supply chain that works as a mitigation to these events is the storage

tanks. Storage tanks play an important role because they can maintain production in case of a shutdown

upstream to the storage facility or maintain production from a platform in case an oil tanker is delayed

or there is an outage at the facility downstream to the storage tank.

In the context of being able to store products, Floating, Production, Storage and Offloading (FPSO)

platforms have a unique design. The basic design of most FPSOs is of a ship-shaped vessel, with


processing equipment (or topsides) aboard the vessel's deck and hydrocarbon storage underneath the

deck, in a double hull arrangement. After processing, an FPSO stores oil and offloads periodically to

shuttle tankers or transmits processed petroleum via pipelines.

Taking the aforementioned example of upstream and downstream outages, the storage facility in the

FPSO plays a central role in its operation. A shutdown may cause problems upstream and downstream to

the storage facility leading to a full shutdown of the field. Upstream failures are typically related to

problems in the platform, but if the export system is still available and a carrier is loading, the system

might be able to complete the loading process before complete shutdown. In this scenario, the carrier

will only be able to take the product available in the storage facility. Downstream problems will cause an

interruption to the export system but production can be maintained up to the point where the storage

tank cannot take more production and a complete shutdown of the plant is required.

Thus, the best operational scenario is that the tanks never reach their maximum or minimum level limits.

In the oil supply chain, the platform feeds the tankers with oil, which is taken to the terminal where the

oil is exported. The export operation is carried out by crude oil tankers.

Among the main tanker classes are:

Table 1. Main tanker classes

Class Length Beam Draft Overview

Coastal

Tanker

205 m 29 m 16 m Less than 50,000 dwt, mainly used for transportation of refined

products (gasoline, gasoil).

Aframax 245 m 34 m 20 m Approximately 80,000 dwt (Average Freight Rate Assessment).

Suezmax 285 m 45 m 23 m Between 125,000 and 180,000 dwt, originally the maximum

capacity of the Suez Canal.

VLCC 330 m 55 m 28 m Very Large Crude Carrier. Up to around 320,000 dwt. Can be

accommodated by the expanded dimensions of the Suez Canal.

The most common length is in the range of 300 to 330 meters.

ULCC 415 m 63 m 35 m Ultra Large Crude Carrier. Capacity exceeding 320,000 dwt. The

largest tankers ever built have a deadweight of over 550,000

dwt.

Once the crude oil reaches the onshore terminal, it is typically fed to tank farms. There are basically

three fundamental operations to tank farms: import of crude oil, transportation between the tanks and,

finally, export of crude oil.

Tanks within tank farms are interconnected, which allows transport of products between tanks. This

procedure is carried out when there are several tanks half-empty and none available to receive the oil.

As tank farms normally receive more than one product, this operation must be carried out to avoid

contamination of products.

The export operations, from the tank farms to the refineries, are generally performed by pipelines.

Pipelines are the most efficient method to transport crude oil and refined products.


Managing the supply chain of oil can be an extremely complex task. In addition to dealing with routine

operations such as planned turnarounds, the analysis must account for unplanned events such as ship

delays, power failures, weather problems and plant outages both in the export system. These events will

cause an interruption to the production to be transported, and it also makes carriers wait, which can lead

to severe penalties. It is important to note that being unable to meet a production demand will cause

direct financial impact, by losing revenue and payment of penalties, but can also cause problems to the

company’s image as an unreliable supplier.

Thus, understanding the risks in each step of the oil supply chain is essential to ensure a profitable and

reliable operation.

3 PERFORMANCE FORECASTING METHODOLOGY

Performance forecasting is a methodology that uses an “event-driven” algorithm based on Monte Carlo

simulation to create life-cycle scenarios of the system under investigation. This methodology is used to

predict the performance of an asset based on the reliability, availability, maintainability and operability of

the system.

A description of each one of these variables follows:

Reliability is the probability of an equipment/system to perform a required function under stated

conditions for a specified period of time.

Availability measures the time where equipment/system is actually operating. The calculation is

based on how often failures occur and how efficient corrective maintenance, taking into account

how quickly failures can be isolated and repaired. Preventive maintenance must also be taking

into account when evaluating the availability of an equipment/system. This can measure based

on how often preventative maintenance (schedule stoppage) is performed and how quickly these

tasks can be performed.

Maintainability is the ability of an equipment/system to be repaired to a specified condition when

maintenance is performed. The maintainability can consider all kind of resources e.g. like

number of personnel available, crew skills, spares, location of the repair, crew and spare.

Operability is the manner on how the system is operated; accounting for operability is essential

to correctly predict the performance of an asset in the oil and gas industry. The initial step when

modelling the operability of an oil and gas asset is accounting for the production profile of the

stream flowing through the system. For an upstream facility, this includes all the wells with

corresponding flow composition that will feed that platform – note that this behavior might be

transient due to phase-in and phase-out of wells throughout the life of the platform. By adding

the production rates, many operation rules can be defined such as degraded failures of

equipment where production is partially loss, production ramping time, storage facilities defined

with size, inflow and outflow, flaring operations and the transportation of the products.

This methodology can be applied during all different phases of a project: from the design phase, to

assess what would be the best technology to be implemented, to the operating phase, to check what the

bottlenecks in the process are and how failures affect the throughput of the unit.


4 CASE STUDY: FLOATING, PRODUCTION, STORAGE AND

OFFLOADING

This study aims at verifying the impact logistics operations can have to the supply chain as a whole.

Here we will focus on transportation from offshore oil platforms to onshore oil storage terminals, which

deliver the oil to the refineries.

As the main purpose of this article is to show how constraints on the export operation can affect the

performance of the supply chain, it is assumed that the refinery does not have any failures during the

life of the system and the only constraint is related to the export operation.

From a basic crude oil processing unit different logistics profiles will be implemented, varying:

Transport resources: number of ships available

Buffer level operating rules

4.1 Description of digital twin

This case study investigates the transport logistics challenges of a Floating, Production, Storage and

Offloading (FPSO) platform. The system to be designed is expected to operate for 10 years. In this

model, the system design capacity is 400 mbbls per day and oil export system is 150 mbbls per day.

There are two oil well fields: Jupiter with two wells and Saturn with four wells; each field has one drilling

center. These wells have similar systems including valves, tubing, jumpers and manifolds. The

production from each field converges to the FPSO, which is then exported using tanker operations.

The FPSO includes a large range of different systems such as oil processing, gas compression and

dehydration, produced water, flare, vent and power generation. In addition to typical equipment failures,

there are losses associated to vessel motion preventing the export operation. The maintenance is

handled by a maintenance crew located on site at the FPSO and extra resources are needed depending

on the equipment failing.

4.2 Maros for performance forecasting

A virtual model of the production field is defined using a range of techniques. The software used to

create the digital twin of the production field is Maros.

Maros is a performance simulator that provides an objective, quantitative approach to systems' design.

Performance is a measure of a system's ability to reach its design requirements, which may be

productivity related, or the need to provide a specific service or function. Maros is an acronym for:

Maintainability, Availability, Reliability and Operability Simulation program.

Maros is a design tool that permits the development and comparison of systems by predicting their life-

cycle behavior pattern. Comparisons can be made of the most elementary concepts in the early stages of

a design project when few details are fixed (or known), while at the other extreme complex systems can

be optimized to yield maximum cost-efficiency. As its acronym suggests, the package encompasses well

known 'types' of analyses that have been successfully integrated into a simulation algorithm and offered

as a design aid. Users need not be specialists nor have an intimate understanding of the mathematics

involved. Emphasis is placed on 'engineering' a system to cope with its lifetime design requirements.

However, this life-cycle approach does introduce the need to consider maintenance and operations of the

system as well as its initial design layout.


Maros was originally developed for the offshore oil and gas industry, where it has been used extensively

to design process facilities and transportation systems to exploit hydrocarbon reservoirs. It is, however,

a general-purpose systems design tool currently used in a wide range of applications including:

Chemicals

Power generation and distribution

Defense

Manufacturing

Transportation, etc.

Its particular applications involve the following aspects:

Equipment reliability and redundancy

Establishing maintenance and intervention strategies

System productivity and sales quotas

System operability assessment

Safety procedures

Risk analysis

Operations research

Maros was prototyped in 1984 and is under continual development.

The following chapter describes the modelling elements used to build the digital twin for this case study.

4.2.1 Block flow diagram

The first, and probably most important step in the modelling process, is to generate a block flow diagram.

The block flow diagram is a logic network that defines the connectivity of mass balance nodes and

focuses on the production aspects of the system. Each node within the network will require its own

reliability block diagram (RBD) that identifies how the system's components are logically connected, and

their operating mode.

Figure 1: Block flow diagram


The dynamic behavior of an upstream system must also be considered. The model must be comprised of

planned changes in the system configuration, such as increasing production rates by commissioning

more wells to the system (or the inverse process when decommissioning wells).

Since commissioning or decommissioning wells will impact the production rates for each stream (oil,

water and gas), two streams are unlikely to maintain a constant ratio. Thus, the impact of failures on

processing equipment will have time variant impact.

For example, consider an oil field that starts its life producing 100 bbls per day and in the second year

there is a flow reduction to 50 bbls per day. Assuming this oil field is comprised of two pumps, and each

pump can handle 50% of the flow. In order to avoid bottlenecks in the system; these pumps must be

designed to take the peak capacity of the oil flowing through the platform. In this particular example, the

rate of 100 bbls per day is set for the first year. So handling 50% of flow means that each pump should

handle at least 50 bbls. This design is fixed, pumps with this capacity have to be purchased and their

capacities don’t change.

Now considering that one of pumps has a failure in the first year – this means that the system loses the

ability to produce at full capacity. Losing one pump means that the system loses the ability to produce

50 bbls per day; i.e. 50% of its capacity. So, the export capacity will be 50 bbls per day. Now assuming

that the same failure occurs five years later when the production has reduced to 50 bbls per day, the

impact will be 0% as the system can continue to work with one pump only. This is an example of a

system that starts its life under stress, with a 2X50% configuration, and toward the end of its life has

more spare capacity, in this case, showing 2x100% of configuration.

The same approach could be applied to different product streams. For example, for an upstream system,

we normally refer to oil, gas and water. Therefore, the compression system and the water production

system will have their production profile associated to them.

4.2.2 Production profile

The characteristics of oil/gas reservoirs are such that the feedstock is a finite and dwindling supply, and

the potential production decreases with time. Furthermore, it is common practice to build up initial

production to a plateau level; hence, initially the potential is low. A similar analogy could be the

expansion of a system to cope with increased demand of a product, e.g. phasing in new assembly lines,

process trains etc.


The following figure describes the production profile considered for this case study.

Figure 2: Production profile

It is important to note the peak production of crude oil (150 mbbls per day) occurring during the first

year. This means the system will have almost no security margin (i.e. every event that causes shutdown

will be critical and produce direct production loss) or boost the system to compensate for production loss

scenarios.

4.2.3 Equipment failures

Equipment failures are defined using statistical distributions. These events are occurrences that cannot

be precisely defined in the timeline but can be estimated using probabilistic distributions. Two sets of

distributions are normally required to define a failure event: a failure distribution and a repair

distribution.

A failure distribution will be used to sample the time to failure and the repair distribution will be used to

define the duration of a repair task (without taking into account maintenance resource evaluation).

Figure 3. Example of failure distribution with MTTF of 1 year defined


Figure 4. Example of repair distribution defined as rectangular distribution with minimum of

24 hours and maximum of 48 hours

The distributions commonly used to define failures are exponential, Weibull and Normal distributions. For

the repair distributions, the distributions commonly used are log-normal, rectangular and triangular.

This case study incorporates mainly reliability data from publicly available sources such as the OREDA

handbook and also factored data to account for the different operating conditions as well as

environmental changes (SINTEF, 1997).

An example of the reliability data defined for the chemical injection system is defined below:

Table 2. Example of reliability data used to describe the chemical injection system

System Equipmen

t item

Failure

mode

Capacit

y Loss

at

failure

Capacit

y Loss

at

repair

Failure

distributio

n

Mean

time

to

failure

(years

)

Repair

distributio

n

Mean

time

to

repair

(hours

)

Process

chemica

l

Injectio

n Pump

Injection

Pump

Critical 100% 100% Exponential 2.16 Constant

Repair Time 13

Degrade

d 0% 100% Exponential 0.83

Constant

Repair Time 8

Electric

motor

Injection

Pump

Unknow

n but

critical

failure

100% 100% Exponential 7.56 Constant

Repair Time 30

Degrade

d 0% 100% Exponential 10.51

Constant

Repair Time 23.8

4.2.4 Planned maintenance

Planned maintenance is defined as scheduled events. These events are occurrences that can be precisely

defined in the timeline with known frequency of occurrence and duration.


A list of three preventive maintenance tasks is shown in the graph below. This graph shows these events

placed in a timeline (X axis) and the length of the box represents the duration.

Figure 5. Planned maintenance schedule

4.2.5 Maintenance resources

A maintenance crew is required to perform repairs for all the planned maintenance activities as well as

corrective maintenance.

4.2.6 Transport logistics

The transport logistics feature in Maros covers all types of transport systems that involve transfer of

products from suppliers to customers. The following common transport modes are catered for:

Rail Car

Barge

Ship

Road Tanker

Conceptually, the approach remains the same with products moving from a supplier (provider/seller) to

a customer (purchaser) via loading points (berths) using a fleet of transport resources.

The standard modelling approach for transport logistics in Maros is time-based, where physical routes

between the customer and suppliers are not important. In the time-based approach, travel times are

defined by the outbound and inbound travel time distributions and analysts can also define travel delays

using cumulative distribution functions.

The building of the logistics infrastructure is largely the same. There are three basic types of elements

relating to the bulk transport modelling. The following section describes each one of these elements:


Table 2. Elements relating to the bulk transport modelling

Node Description Icon

Berth

Node

Identifies loading points. One of the key features of a berth is the ability to

restrict its operating periods (i.e. service constraints).

Bulk

Transport

Node

Identifies groups of bulk transport resources. A bulk transport item contains

many bulk carrier members (initially populated with one); each bulk carrier

represents a transport resource within the items’ fleet.

Storage

node Identifies points where buffer elements are defined.

A berth acts as a gathering point for import or export (or even both) transactions. A berth is normally

connected on one side to a storage tank and the other to a bulk transport group resource. The mode of

operation will be determined by the layout. For example, export operations will have the product store

upstream of the berth, and conversely import operations will have the product store downstream of the

berth.

To fully define a berth, analysts must set up a few important parameters:

Load rate

Dock reference

Berths are grouped under a parent dock reference; this allows queuing to be managed at a higher group

level; which in turn allows more complex operations to be simulated.

4.2.6.1 Bulk transport nodes

The bulk transport node contains a group of transport resources that share the same functionality; the

group may be comprised of a single entity or an entire fleet.

The definition of the bulk transport includes the following parameters:

Characteristics including size of tanker, transfer rate, embarking and disembarking time etc.

Supplier/customer relations, which include defining the travel time, which can be described as a

statistical distribution or call-up events for the tanker

Berth control describing level management that can be used to control the berthing process

Maximum capacity

Maximum transfer rate


4.2.6.2 Connection arrangement

Several configurations can be used to model a logistic scenario, but the basic rules that should be

followed are:

A berth must be located both upstream and downstream of the oil carrier.

A tank must be provided both upstream and downstream of the berth.

Figure 6. Example of Connection arrangement

At start-up, the first bulk carrier commences loading at the supplier. Any other carriers within the group

(fleet) will be equally spaced out between leaving the customer side and the loading point. It may be

prudent to have an initial level in the import store, otherwise there will be no production taking place

until the first load is transferred to the import store.

Once loaded the carrier sets off for the customer using the outward-bound travel distribution. Operations

such as disembarking time can be modelled as there is a berth at the supplier side. On arrival at the

customer berth location the carrier will commence berthing if the following conditions are met:

The berth is not already in use by another carrier

The berth is open

There are no berth operation delays occurring

If any of the foregoing occurs the carrier is placed on the unload queue and will be eventually berthed in

the order in which it was placed in the queue.

Once berthed the unloading operations will commence if the following conditions are met:

The berth loading periods are active i.e. loading permitted

There are no equipment failures stopping the load transfer to the import store.

Once emptied, disembarking operations will take place if the following conditions are met:

There are no travel faults occurring

There are no berth operation delays occurring

On completion of the disembarking procedure the carrier is marked as being ready for another load and

immediately sent to the supplier for another load using the inbound travel distribution. If the supplier

side is busy on arrival the carrier is placed on the load queue and will eventually be loaded in the order

that it entered the queue.

4.2.6.3 Definition of the logistics operations

Initially, the model will assume a fleet that is composed of four crude oil carriers, each being able to

carry 3 million barrels; this falls within the category of ULCC. There are a small number of ULCC vessels

currently in use, as their size requires special facilities limiting the number of places where these vessels

can load and offload. These massive vessels can carry around 2 million barrels to 3.7 million barrels of


crude oil. The size of the fleet is calculated based on the peak production of crude oil which occurs in

2060, the third year of production where all wells are commissioned. The timeline to complete a full

delivery is described as follows:

2 days to load

1 day to travel to the customer

2 days to unload the cargo

1 day to return to load more production.

It is expected that the operation is completed within 6 days when the oil tanker is back to the FPSO to

load more crude oil.

The simulation will try to optimize the number of crude oil carriers utilized throughout the life of the

asset. The model is defined with to allow overtaking in the export queue - this allows overtaking in the

queue in the export side for members of the same fleet only, such that the minimum numbers of

members in the fleet are used.

As mentioned before, only a few ports around the world have the infrastructure to accommodate a ULCC.

Thus, for this case study, it is assumed that only one port is available to receive the cargo. This port

operates between 6am and 6pm, which means that arrival and departure times will be restricted. The

restriction on the arrival and departure will impact the commencement of berthing and disembarking

operations. These operations cannot commence before the initial arrival time in any day and they must

be completed before the last departure time in any one day. Note that failure to complete either

operation will constitute a complete restart the following day, i.e. these operations cannot be suspended

and resumed the next day.

In addition to the travel time, defined as one day, the probability of the carrier being delayed is also

accounted for. This might be caused by metocean condition, vessel traffic etc. The probability of being

delayed is described as:

• There is 10% probability of being 1 day late

• There is 50% probability of being 0.5 day late

• There is a 40% probability of being 0.25 day late

Finally, the level of the storage facility is what controls the call-up of oil tanker. To fully define the buffer

level management rule, two values need to be defined in the threshold range. The first value (from)

indicates the percentage of the tank volume that will initiate the call-up. The second value indicates the

percentage of the tank volume that will end the call-up. Note that if a group of resources is loading at

the moment the call-up ends, the loading will be completed and the fleet will travel to its destination

before the tanker go back to the idle condition.

The following schematic shows how the software tool simulates the threshold being used:


Figure 7. Operating rules description

In this case study, the range considered to operate the call-up of tanker is between 80% where the call-

up will be initiated and 70% where the call-up will be ended. These values are selected based on the

assumption that the likelihood of getting outages upstream the storage facility (i.e. the platform) is

much higher than downstream the storage facility (i.e. transport logistics).

5 RESULTS

The results of the model without the transport logistics modelled will be used to create a benchmark for

the study of the logistics operations.

The main key performance indicators used to compare the different cases are:

Production efficiency (or deliverability according to ISO 20815)

Annual production efficiency and volume per year

Average volume produced (mbbls) and product loss (mbbls)

Utilization of the oil tankers

Probability of non-exceedance curve for the storage tank

Where necessary, more results will be used to explain the behavior of the simulation.

The simulation is run for 100 cycles representing 100 feasible lives of the system.

Bulk TankerHigh Threshold

Low Threshold

Initiating BT call up

Ending BT call up

Call up of Bulk

Transport resources

initiated by a rising

level in a tank

Bulk TankerHigh Threshold

Low Threshold


Ending BT call up

Call up of Bulk

Transport resources

initiated by a rising

level in a tank

Bulk Tanker

High Threshold

Low Threshold


Ending BT call up

Call up of Bulk

Transport resources

initiated by a falling

level in a tank


The production available for the system without the transport logistics modelled is: 89.65% with a

standard deviation of 1.1%. The average volume produced over the system life is 267,958 mbbls and

the average production loss is 30,939 mbbls.

The following graph shows the annual production efficiency and volume per year:

Figure 8. Annual production efficiency and volume for base case

The blue portion of the bar indicates the average production loss per year, whereas the green portion

shows the average volumetric production per year. It is important to note that during year 1 to 3, the

system is under stress with full capacity being utilized. That is why production loss is very critical at

these two periods of the life of the asset.

For the benchmark model, where no logistics operations are modelled, the Utilization of the oil tankers

and probability of non-exceedance curve for the storage tank are not available.

Now, by adding the logistics operations to the benchmark model, it is possible to isolate the impact of

constraints in the logistics to the overall system performance. The production availability when the

logistics are taken into consideration is: 87.368% with a standard deviation of 0.866%.; a reduction of

2.3% when compared to the base case.

The average volume produced over the system life is 261,141 mbbls and the average production loss is

37,756 mbbls. This represents a reduction of over 7,000 mbbls of transported product. Considering the

price for an oil barrel at USD$50, 00 per barrel, this represents a production loss of close to USD$680M –

this difference is displayed in the following graph:


Figure 9. Compared cash-flow of base case with and without logistics

The annual production efficiency and volume per year shows a very similar pattern when compared to

the base case result:

Figure 10. Annual production efficiency and volume for base case with shipping

Cumulative Cash; -1276,968

Cumulative Cash; -1549,625-3000

-2500

-2000

-1500

-1000

-500

0

Cash

-flo

w (

US

D $

)


The Utilization of the oil tankers is described by the following graph:

Figure 11. Utilization of carriers

It can be noted from the graph above that the Utilization of Carrier 1 is slightly higher when compared to

Carrier 2, 3 and 4 – remember that the simulation engine will try to optimize the number of the carriers

utilized throughout the simulation process. The operation statistics graph shows the average time taken

by each operation throughout the simulation process:

Figure 12. Group averages for operations statistics


Breaking down into more details, the result per member in the fleet shows the following information:

Table 3. Operations statistics per member

Bulk

Transp

ort

Travel To

Supplier

(days)

Loadin

g

(days)

Travel To

Customer

(days)

Unloadi

ng

(days)

Queue to

Load

(days)

Queue to

Unload

(days)

Idle

(days

)

Oil

carrier

1

416.92 626.906 416.636 574.866 100.107 44.0308 1470.

53

Oil

carrier

2

760.926 486.159 284.989 392.889 230.236 34.6746 1460.

13

Oil

carrier

3

722.121 582.969 282.763 390.264 478.096 35.1701 1158.

62

Oil

carrier

4

584.816 688.737 313.054 382.92 899.603 33.0267 747.8

42

There are two values that show a big difference between the members of the fleet:

Queue to load; which is the total average time (in days) spent by the resource queuing to load a

cargo.

Idle; a carrier is said to be in idle condition if it is empty and ready for next load but the berth is

occupied by another resource.

The difference between “idle” and “queue to load” is that in the “queue to load” there are no cargos in

front that prevent loading; the queue is only caused by berthing constraints or failures.

This aligns with the current modelling scenario. Carrier 1 and Carrier 2 will complete the supply chain

route and return to the FPSO to load more crude oil; given potential failures and disruptions to the

loading process, Carrier 1 and Carrier 2 are likely to wait until Carrier 3 and Carrier 4 finish the loading

process.

At the same time, Carrier 3 and 4 are likely to experience more disruption from failures in the FPSO than

Carrier 1 and Carrier 2. Since Carrier 1 and Carrier 2 will have loaded a large portion of the crude oil

stored in the tank, Carrier 3 and Carrier 4 are more dependent on the production coming directly from

the FPSO.

An average of 1125 days of idleness means that the fleet is not highly utilized throughout the life of the

system – or highly utilized only during a specific period of time.

Finally, the probability of non-exceedance curve for the storage tank is displayed in the following graph:


Figure 13. Probability of non-exceedance for logistics operations

This graph indicates the probability of having the tank level above a certain range. For example, the

graph is showing that there is a 95% probability of the buffer tank not exceeding 94% of the tank

capacity. The buffer efficiency report shows the average number of full-up events equals 164.2 and

percentage of time when the tank was full equals 2.21%. The full-up analysis shows that full-up events

are occurring throughout the entire life of the asset. This displays the behavior for the cycle number 1

only.

Figure 14. Duration of full-up for logistics operations

The top-out is a critical event which will shut down the entire facility. Thus, it is important to manage the

level of tank to avoid top-outs. The following section discusses potential sensitivities to improve the

efficiency of the supply chain.

6 SENSITIVITY ANALYSIS

The current operating rule for storage facility is leading to top-out during the period of peak production.

The current buffer level management has a threshold of 80%, where the call-up will be initiated, and

70%, where the call-up will be ended. Analyzing the highest value of the range, 80% of the storage

facility, gives 60 mbbls of volume before the tank tops-out. This means that during the peak production

period of 150 mbbls per day, the facility has only 9.6 hours of buffer in case of a tanker being late. In

addition, during period of high production, the storage tank takes 2 days to be completely full, whereas


it would take, on average, more than 4 days if the average production from year 5 to 10 is taken as a

reference.

This also helps to explain why carriers appear to be idle for a long period of the system’s life. This result

aligns with the expectation, since the logistic planning has been designed with the peak production in

mind. The excessive number of carriers will have a direct impact on the operational expenditure of the

supply chain, i.e. more carriers (being utilized or not) will represent more cost.

These problems are further investigated by running two sensitivity cases:

Different operating levels for the tank

Number of carriers

The sensitivity analysis will be broken down into two periods: period 1 assessing the peak of high

production, from year 1 to 5, and period 2 assessing the tail of the production curve.

6.1 Sensitivity 1: Operating rules

Two new ranges of operating rules for the storage facility are tested using the digital twin of the asset:

Case 1 (Base case) 80% - 70%

Case 2: 70% - 60 %

Case 3: 60% - 50 %

The expectation is that lowering the range of operation for the storage facility will allow more time in

case the carrier is delayed. Comparing the probability of non-exceedance graph for each case shows the

line moving to the left. This means that, for the same probability, the case with the lowest range will

have a lower level as a reference.

Figure 15. Comparison of probability of non-exceedance

For Case 2, the buffer efficiency report now presents an average of 76.4 occurrences of full-up events

and for Case 3, an average of 28 occurrences. The percentage of time when the tank is full is 0.72% for

0

10

20

30

40

50

60

70

80

90

100

1 4 7

10

13

16

19

22

25

28

31

34

37

40

43

46

49

52

55

58

61

64

67

70

73

76

79

82

85

88

91

94

97

100

Probability of Non-Exceedance

Case 1

Case 2

Case 3


Case 2 and, for Case 3, only 0.25% of the time. Moreover, in the full-up analysis the results clearly state

a lower range will significantly decrease the number of full-up events for the storage tank.

Figure 16. Comparison of duration of full-up

The full-up analysis also indicates that the range of 60% - 50% works for the period of constant

production but the system is still showing top-outs for the first three years of production.

The improvement in the storage facility is reflected in the efficiency of the system – as aforementioned,

full-up events should be avoided as they would shut down the entire facility. The efficiency improvement

is shown in the graph below:

Figure 17. Comparison of production availability

The overall improvement is almost 2%, which represents another 5.8Mbbls transported. Another

improvement refers to the standard deviation – the standard deviation goes down to around 0.9%. This

number is generally high but the reduction means that the model’s behavior is more predictable.

In order to investigate how the tank operating range behaves for the period of peak production, the first

five years, this part of the model is isolated. For this period, another three cases are investigated:

0

50

100

150

200

250

300

350

400

2060 2061 2062 2063 2064 2065 2066 2067 2068 2069

Du

rati

on

of

full

-up

Years

Case 1

Case 2

Case 3

86

86,5

87

87,5

88

88,5

89

89,5

90

90,5

Case 1 Case 2 Case 3

Improvement

Efficiency


Case 1 (Base case) 55% - 50%

Case 2: 50% - 45 %

Case 3: 45% - 40 %

Upon running the three sensitivity cases, the results for the full-up data show a minimal difference of

around 7 top-outs between cases – Case 1: 21 occurrences, Case 2: 14 occurrences; Case 3: 8

occurrences. The efficiency changes on the range of 0.1% but it is important to note that the standard

deviation of the model is 1%. This means that this change is still within the expected deviation of the

model prediction. Thus, the assumption is that these sensitivity cases do not really impact the

performance.

The next sensitivity aims at avoiding tank top-outs by controlling the feed of the tank. This operating

rule is based on the level of the tank – once the tank rises above a certain level, the event is triggered

and the feed rate is reduced by 25%. The cases investigated are:

Case 1: Rising above 60%



The new approach shows a decrease on top-outs: Case 2 and Case 3 with 5.5 occurrences and Case 1

with close to zero. However, this impacted directly the production efficiency – all cases have shown a

decrease ranging from 1% to 1.5%. Thus, this sensitivity is not accepted.

A range of other sensitivities could be tested – more ranges for the aforementioned rules, buffer level

management controlling the entire production (not only the oil export), definition of a different fleet with

smaller carriers to cope for tank top-outs in case the ULCC are unavailable.

It is also important to note that the system will be optimized only to a point and there is some inherent

unavailability that must be accounted for in the calculations.

6.2 Sensitivity 2: Number of ships

Taking each period in isolation and evaluating the carrier Utilization shows that for the second period,

where production is constant, the four carriers defined for the base case spend a lot of time in an idle

state. This makes sense as the simulation will try to optimize the number of carriers utilized throughout

the system life.


Transport; 2,5 Travel To Supplier (days);

275,462Berthing (days);

0,0001Loading (days);

288,05325

Travel To Customer

(days); 127,49425

Unloading (days);

175,6395

Queue to Load (days);

223,694725

Queue to Unload (days);

11,2368

Idle (days); 698,419975


The period of constant production has a smaller production rate giving more time for carriers to complete

the journey from the customer. Therefore, by the time another carrier is required, one might already

have returned and it will overtake the next one in the queue. The question is: Is it possible to reduce the

number of carriers? If so, how many carriers are necessary to maintain the production efficiency?

To understand the impact of carriers on the performance of the supply chain for the second period of

constant production, three cases have been analyzed:

Case 1 (Base case): 4 carriers

Case 2: 3 Carriers

Case 3: 2 Carriers

The results are compared in the following figure:


Case 3 shows the smallest time spent in idle but it also shows a decrease in efficiency of 1.5%. Case 2

shows exactly the same production efficiency but one less carrier. Thus, Case 2 is selected as the

operating scenario for the last 5 years of production. The expenditure saving for this improvement is

equal to hiring only three ULCCs instead of four for the last 5 years of operation.

7 CONCLUSION

Performance forecasting is a methodology based on RAM analysis specifically designed to cover the oil

and gas modelling needs. This methodology has been an important tool for design optimization but there

is a great shift in the market to start applying this method during the operational stage. However, the

ever-changing state of an oil and gas production system poses many challenges to performance

prediction studies. This is especially true for logistics operations where a number of variables are

dependent on transient or seasonal (e.g. metocean data).

The existing digital twin created during the design stage can be extended to account for logistics

operations and important decisions about carriers’ characteristics and storage level management can be

made.

0

100

200

300

400

500

600

700

800

900

1000

Carrier 1 Carrier 2 Carrier 3 Carrier 4

Idle

(days)


The case study investigated in this paper shown many top-outs which have been mitigate by

manipulating the buffer level management rule which is used to describe the call-up events. Moreover,

the case study was broken down into two periods, a period of high production (from year 1 to 5) and

period of constant production (from year 5 to 10). This allowed the decision-making process to be

broken down into specific periods of the system life, allowing for local decisions (i.e. per year) instead of

global decision (i.e. entire system life).

The following graph shows the improvements over the base case:


After improving the efficiency for the period of peak production, the second period – the constant

production period – was the focus. The original plan for the number of carriers (which was based on high

production capacity) was not suitable because production during this period was much smaller. The

number of oil carriers was too high, leading to carriers operating in an idle state for a long period.

Figure 21. Operations statistics for Oil carriers during period 2

After testing three cases, Case 2 was selected, the same production efficiency but one less carrier. Thus,

Case 2 is selected as the operating scenario for the last 5 years of production. The expenditure saving

for this improvement is equal to hiring only three ULCCs instead of four for the last 5 years of operation.

86

86,5

87

87,5

88

88,5

89

89,5

90

90,5

Case 1 Case 2 Case 3

Improvement

Efficiency

Transport; 2,5 Travel To Supplier (days);

275,462Berthing (days);

0,0001Loading (days);

288,05325

Travel To Customer

(days); 127,49425

Unloading (days);

175,6395

Queue to Load (days);

223,694725

Queue to Unload (days);

11,2368

Idle (days); 698,419975



Even though operational procedures are important, reliability data are still fundamental for the creation

of the RAM study and for the success of the decision-making process. Reliability data remain a big

challenge for the oil and gas industry and are largely unavailable.

8 REFERENCES

Ballou, R. H., 2003. Business Logistics Management. s.l.:Prentice Hall. Calixto, E., 2016. Gas and Oil Reliability Engineering: Modeling and Analysis. 2nd ed. s.l.:Elsevier Science & Technology Books. DNV GL, Software unit, 2013. Maros and Taro - prime tools for predicting performance. [Online]

Available at: https://www.dnvgl.com/cases/shell-global-solutions-4051 [Accessed 2017]. DNV GL, S. u., 2016. Maros Manual 9.3. London: DNV GL, Software. Energy Information Administration, 2014. Oil tanker sizes range from general purpose to ultra-large crude carriers on AFRA scale. [Online] Available at: http://www.eia.gov/todayinenergy/detail.cfm?id=17991

[Accessed 2017]. Frazelle, E., 2001. Supply chain strategy. s.l.:McGraw-Hill.

Hugos, M. H., 2011. Essentials of Logistics and Supply Chain Management. 3rd ed. s.l.:John Wiley & Sons. Manzano, F. S., 2006. Supply chain practices in the petroleum downstream. Massachusetts Institute of Technology. Engineering Systems Division. NTNU, S., 2015. Offshore and Onshore Reliability Data. 6th edition ed. Norway: s.n.

Standard, 1991. IEEE Guide To The Collection And Presentation Of Electrical, Electronic, Sensing Component, And Mechanical Equipment Reliability Data for Nuclear-Power Generating Stations. s.l.:IEE.

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Carrier 1 Carrier 2 Carrier 3 Carrier 4

Idle

(days)

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