Abstracts

Spectra Energy’s expansion of its natural gas facilities in today’s ultra competitive energy markets is driving a need for enhanced risk management. Tools such as Crystal Ball are key to the risk analysis that needs to be performed in order to remain aware of and manage project risks effectively. Schedules and cost need to be balanced and considered together.

This session highlights the concepts behind integrated schedule and cost modeling using Crystal Ball. It also identifies opportunities to balance and optimize project outlooks utilizing Crystal Ball’s optimization tool, OptQuest.

Forecasting Reserve Potential From Horizontal Wells Using Crystal Ball

David F. Yard, PE, Reservoir Engineer, Field Studies Group-EOR, Chaparral Energy LLC

The drilling of horizontal wells gained momentum in the decade of the 90s. There has been much confusion in the oil and gas industry as to what conditions constitute a good horizontal candidate. Most of the confusion arises from the fact that engineers need to look at the reservoir in 3-Dimensions instead of the usual 2-Dimensions associated with traditional vertical wellbores.

Horizontal drilling is still a major buzz word associated with IPOs and fund raising from NY bankers and investors. Although horizontal drilling is perhaps the best thing to hit the oil patch since the rotary table, engineers and investors need to know how, when, where, and why they work or don’t work.

I will be presenting a simple; actual rather complicated model which is simple to use. The model is simplified by using the production characteristics of offsetting vertical well to model the reserve possibilities associated with a horizontal wellbore in the same producing horizon. The model was initially developed in 1989 and has been used in consulting projects around the world. However incorporating CB with the model enhances our ability to predict reserve potentials and sensitivities to economic outcomes.

Shale Gas Resource Evaluation Using Monte Carlo Simulation - A Straightforward Way to Estimate Recoverable Gas

Robert K. Merrill, President, Catheart Energy, Inc.

In today’s highly competitive oil and gas business it is critical to accurately assess potential resources associated with available opportunities. This is true whether one is evaluating conventional oil or gas resources, or resource plays such as shale gas. Making informed decisions relies on assessing the range of future production based on the range of an expected resource in place. Estimating resource volumes for conventional plays depends on estimating net reservoir pay, reservoir area, porosity, hydrocarbon saturation and recovery factors. These parameters are modeled in a straightforward way using Monte Carlo simulation to understand the range of outcomes.

However, shale gas resource evaluation is dependent on a different set of parameters that are associated with the nature of the shale and organic matter. Using shale density, shale thickness, total organic carbon, hydrogen index and organic maturity it is possible to use Monte Carlo simulation to evaluate the recoverable gas resource. In addition to the probability of outcomes, the simulation results highlight the parameters most critical to the outcome through sensitivity charts and scatter plots. Investment decisions can then be made with full understanding of what attributes of the play have the most uncertainty and what the range is of the expected recoverable resource.

Stochastic Time Series Forecasting of Future Energy Prices

Steve Hoye, Senior Risk Consultant at Oracle’s Crystal Ball GBU

Forecasting future oil and natural gas pricing is difficult and fraught with pitfalls. In this presentation, the author looks at historical oil prices with an eye toward using time series analysis to build pricing forecasts that can be used to realistically represent possible future price scenarios in a Monte Carlo simulation. After discussing the issues that are presented by the historical data, several different approaches are illustrated using CB Predictor, along with other data analysis tools in Crystal Ball.

Using Uncertainty Enlightenment for Field Development Planning and Management - A Case Study

Susan Peterson, Adjunct Lecturer, Rice University

An operating company faced the challenge of a marginal offshore field development by using uncertainty enlightenment to assist in multiple critical decisions.

Some of the planning and management issues that were resolved after using probabilistic modeling were:

Whether to re-enter and work-over existing wells or drill new wells;
How to quantify the geologic uncertainty and risk relationships across multiple layers and multiple fault blocks;
How to sequence the wells based on (a) meeting FPSO hookup schedule (b) economics, and (c) reservoir information, including a nearby discovery for satellite tie-back; and
Whether to buy or lease an FPSO considering the likely field life, and, for each option, the relative attractiveness of the commercial terms.

This presentation will exhibit the extent to which probabilistic full field modeling allowed discussions to be centered on quantifiable risks and uncertainties and allowed management development decisions to be based on the uncertainty enlightenment that resulted from those models.

Digital Oilfield Solutions

David Shimbo, Senior Director Industry Solutions, Oracle

Oracle is committed to delivering Digital Oilfield solutions to the Oil & Gas business. Oracle's solution focuses on providing an E&P data management framework that leverages an Application Integration Architecture. This allows oil companies to implement a best-of-breed application environment with integrated data access across the enterprise. This presentation will discuss the Digital Oilfield in the context of Application Integration Architecture and Master Data Management. Several use cases will be reviewed that will show how business intelligence tools can be used to integrate and analyze well data, operational data, financial data and supply chain logistics.