Syncopation Software

Oil and Gas Examples

Oil and Gas Example #1

Techniques:
  • Strategy Tables
  • Influence Diagram Arrays
  • Time Series Percentiles
  • Asymmetry
  • Downstream Decisions
  • Multiple Attributes
  • Perform Subtrees
  • Learning

DPL Decision Tree Model for Gas Export Strategy

Whether you're producing oil, gas or minerals, it has no value until it's brought to market. With the pursuit of deposits in increasingly inconvenient places, consideration of the costs and risks of transportation are often a major driver of the viability of a project.

Use the following link to download the DPL example model:

Model: Gas Development - Energy Case.da




Oil and Gas Example #2

Techniques:
  • Asymmetry
  • Downstream Decisions
  • Multiple Attributes
  • Probabilistic Conditioning
  • Perform Subtrees
  • Learning

DPL Decision Tree for LNG Regas Opportunity

Natural gas in it's liquid form (LNG) can skirt pipeline infrastructure and instead be transported by special ships to LNG import facilities for regasification to then be distributed to market. Recently, the costs associated with building these LNG import and export facilities and transportation mechanism has risen dramatically. At the same time the US is experiencing a natural gas boom. Consequently, many firms are now scrambling to re-invest capital to convert or add export capabilities to their LNG import facilities.

The final investment decision to build, convert, or increase capacity is plagued by a variety risks and uncertainties -- including regulatory hurdles, commodity prices, natural gas price volatility and market swings. DPL can provide a comprehensive, coherent framework for incorporating all these drivers of uncertainty and value, and produce a transparent, defensible recommendation.




Oil and Gas Example #3


Techniques:
  • DPL Value modeling
  • Downstream decisions
  • Multiple get/pay expressions
  • Asymmetry
  • Imperfect information
  • Learning

DPL Decision Tree for Wildcatter Decision

To drill or not to drill? It sounds simply enough -- but in reality a wildcatter has more choices than just whether to drill or not. They typically have the option to conduct one of several tests intended to provide imperfect information on the likelihood of finding oil and how much. When a decision (Drill?) is preceded by a chance node (Test) that provides imperfect information, we say that the model contains learning. Test drilling for oil is a classic example in which a learning model can be employed to find the real value of an asset.

Use the following link to download the DPL model:

Model: Oil Exploration - Wildcat.da



Oil and Gas Example #4

Techniques:
  • Asymmetry
  • Downstream Decisions
  • Perform Subtrees
  • Learning
  • Halt function
  • Allow event already active

3 Well Sequential Drilling Decision Tree Model in DPL

A common and challenging decision problem in the oil & gas industry is to decide how to explore an oil field. Typically there is a cluster of prospects, giving rise to a highly dependent set of uncertainties, according to their proximity and the geology of the area. For example, if you drill a well at one site and strike oil, it's more likely that a nearby prospect will also have oil. If you drill a few "dry holes", you'll probably give up on the field rather than throw good money at potentially poor prospects. This makes intuitive sense, but until recently it was tough to model in a decision tree. With DPL's handy Pruned Sequential Tree feature -- these types of sequential decision problems are easier to tackle than ever before.

Use the following link to download the DPL example model:

Model: Sequential Drilling Problem - 3 and 6 Wells.da