Our blogs Blogs home
Software

Software

Plant

Supply Chain Modelling: Shipping and Logistic Operations

One of the key challenges of the oil and gas industry refers to the increasingly complexity of its supply chain. Maros and Taro allow users to take into account key aspects of Supply Chain Modelling: Shipping and Logistics Operations.

The demand for shorter and reliable delivery times for oil and gas products play a key role on the world-wide chain of supply. Modelling this scenario which involves assets such as upstream platforms, LNG, refining and petrochemical plants is not an easy task. For these assets, production efficiency is a complex interaction between reliability, blending and yield rules, flow routing (including recycle), and intermediate storage options so it could be very time-consuming. With the advent of computational models, an opportunity of dealing with such a complex scenario has been made.

When the analyst has information related to the key drivers of performance inside a supply chain modelling, informed decision can be made. The Logistics Operations functionality in Maros and Taro account for complex scenarios when studying the logistic challenges of transporting products in the oil and gas industry.

Four basic areas of major concern

Assessing and integrating all the key factors of supply chain can be time-consuming with all the information and possible interactions. There are basically 4 areas of major concern: shipping, storage and berth facilities, scheduling of ship arrival and the integration of all these factors to with operated assets.

Four areas of concern

Four areas of concern

The questions that you are trying to answer in the context of Shipping and Logistics Operations are typically:

Answers!

Questions?!

How can Maros and Taro help me out?

Maros and Taro is a suite of simulation software used to predict asset performance based on the well-established Reliability, Availability and Maintainability (RAM) methodology. It includes an event-driven dynamic approach centred on the Monte Carlo method accounting for different types of event such as equipment failure, maintenance tasks and typical operational processes.

Traditionally, RAM analysis creates different life-cycles of a system taking into account its design configuration, maintenance strategy and operational procedures. A life-cycle is a chronological sequence of events which represents one possible behaviour of the system. Maros and Taro can generate an infinite number of such events, each one being unique but also being a feasible representation of how the system could actually behave during its life. These events are fundamental occurrences in the system which are used to estimate the system effectiveness to perform a specific task.

In the oil and gas industry, these events are equipment failure, planned maintenance activities and logical events such as operational logic. In order to estimate the occurrence of such events, Maros and Taro uses a pseudo-random sampling techniques which enables the software to cope for vast number of possible lives of the system.

The analyst replicates the design configuration and operational logic in a virtual model of the system. When the simulation starts, the software then moves from one “state” to another controlled by the estimated sequence of events. A large number of variables representing the system are changed in an occurrence of event. As the simulation progresses, new events are estimated moving towards the specified design life and the simulation finishes when the times exceeds it.

What factors can I take into account when modelling Shipping and Logistics Operations?

In order to completely cover the Shipping and Logistics Operations, the modelling process has to be able to account for all types of transport systems that involve movement of batches of products from Suppliers to Customers, i.e. the following common transport modes are catered for:

  • Rail Car
  • Barge
  • Ship
  • Road Tanker

Conceptually, products move from a Supplier (provider/seller) to a Customer (purchaser) via loading points (Berths) using a fleet of transport resources. Regardless of the mode employed, the building of the logistics infrastructure is largely the same:

Taking for example a typical oil supply chain:

Logistics Operations: Exporting -> Berthing -> Ships -> Berthing -> Importing

Logistics Operations: Exporting -> Berthing -> Ships -> Berthing -> Importing

To simplify the approach let us break the supply chain into 5 different “assets”.

The first asset refers to the exporting system – for crude oil this is typically the offshore platform. The offshore platform is responsible for extracting the crude oil and typically include operations such as separation and treatment to stabilise the crude oil.

Once the crude oil is stable enough, it is sent to a storage tank where it is stored until a crude oil tanker is available. The crude tanker starts to approach the platform and it is connected to the transfer system. This one starts transferring crude oil from the platform to the tanker. Next step refers to the tankers carrying the crude oil from platform to customer delivery point. Finally, at the delivery point a similar configuration exists with a berthing operation and import terminal to receive the cargo.

Now in order to calculate the production efficiency (or number of deliveries) of this system, there is a large number of interrelated variables to be accounted for. For instance, each one of these assets actually require a number of factors to be defined before getting a complete picture of the performance.

parameters-to-take-into-account

Logistics Operations: Parameters to be assessed per Asset

So a number of questions must be answered – for example, considering a storage tank in the import or export terminal, we could potentially ask:

What capacity?

  • Too much capacity basically means unnecessary investment. However, too little also means being unable to meet demand at peak times – there is no flexibility.

How many tanks?

  • Too few tanks – they are normally too large with deep foundations. We would benefit because it takes less overall space but there are also some safety concerns if tank ruptures . It is kind of having “all eggs in one basket”.
  • Too many tanks – incredibly large land area required, less foundations. It is easier to operate as you tend to have more flexibility but also there is more boil-off (if we are talking about gas).

The Modelling process

There are, basically, two ways of modelling transport logistics:

  • Time-based: where physical routes between the customer and suppliers are not important and travel times must be defined using travel time distributions. This should also include travel delays
  • Travel-based: where physical routes are modelled as a set of legs on the journey. Each leg must attribute distance, speed limit, events encountered by vessels traversing it (e.g. bad weather)

The modelling process should be able to cover the following key problems:

  • Single and multiple berths can be modelled, with each berth being used by an unlimited number of fleets
  • Each fleet or ship can be associated with a product and routed to a particular group of tanks
  • The entire supply chain can be modelled including:
    • Multiple production locations and multiple ship loading locations
    • An unlimited number of routes from the loading point to the unloading point
    • An unlimited number of unloading points

Berthing operations

Berth are loading or unloading point for hips. A berth can be used by multiple ships and multiple products. An unlimited number of storage tanks can be linked to each berth. So the analysis should be able to account for:

  • Number of berthing spots
  • Berthing restrictions (daylight, weekdays, etc)
  • Berthing delays (probabilistic)
  • Time required for mooring and berthing
  • Time required for departure
  • Berthing rules:
    • Berthing is only allowed when sufficient ullage or inventory is available
    • Multiple berthing locations but unloading limited to one
  • Unloading/loading rate (for each ships)
  • Unloading/loading rate (for each ships)

So what value this analysis adds to the business?

By understanding the system’s performance, decisions based on science can be made! These decisions range from:

  • Spot cargos
  • Annual Delivery Plan
  • Financial investment decisions
  • Validating send-out guarantees in contract negotiations
  • key performance drivers and bottlenecks in different stages of the project

The very small margin where the oil and gas industry is operating makes savings even more important now. In order to succeed in this environment, answering the following questions will be fundamental:

  • How often and for how long would terminal send out be interrupted, either as a result of running out of stored product or due to mechanical and operational outages of the terminal?
  • What are the risks of disruption to the shipping schedule?
  • How long would a ship be forced to wait in order to unload its cargo and when should a ship be rejected by the terminal?

It is all about planning (I mean, good planning).

0 Comments Add your comment

Reply with your comment

Your email address will not be published. Required fields are marked *