Toolbox v5-1

Interrogate your analog data and apply curve fit distributions to make better resource assessments!

Toolbox software provides an easy and intuitive application for analyzing analog data and improving your uncertainty estimates for exploration and development resource and chance of success estimation.

Tools include:

Enhanced Plotting and Curve Fitting to Analog Data
Creaming Curves
Ternary Plot function
Chance of Success and Dry Hole Analysis Tools
Methods for building Lognormal, Beta, and Normal Distribution based on a minimal number of inputs
Gas Expansion Factors, Oil Formation Volume Factor and Condensate Shrinkage calculators
Analyze all input and output detail from multiple MMRA files to assist with quality control and reality checking

Enhanced Plotting and Curve Fitting

This function allows users to understand the appropriate input distribution from analog data sets for resource assessment. Toolbox provides an easy import analog data and transfer filtered data sets to the Enhanced Plotting function. Imported data can be filtered by numeric, text or data values and lognormal, beta, normal and polynomial curve fits can be applied to determine the best distribution shape and percentiles can be used for modeling in probabilistic resource tools including MMRA. Key analog wells can be highlighted to assist with reviewing the analog data sets. Both percentile and numeric value clips can be applied to disregard outliers from the analog data sets.

Toolbox
Figure 1: Users can selectively import data from any Excel Workbook using numeric ranges or matching text criteria for plotting and curve fitting in the Enhanced Plotting Function.

Figure 2: Imported data are plotted on several charts including a log probit with multiple curve-fits for modeling in probabilistic software.

With a limited data sample, fit a lognormal curve to match the sample average. Then modify the slope of the curve to match an analog play’s P10:P90 ratio and build an envelope of lowside and highside lognormal curves. These curves encapsulate all the small sample’s markers in recognition of the uncertainty of the population’s actual mean value.

Figure 3: With limited data, a lognormal curve with the mean matching the sample average, the curve’s P10:P90 ratio can be modified to match analog data and fit an envelope of lowside and highside curves encompassing all the data values to represent the uncertainty of the mean.

Creaming Curves

Creaming Curves plot every input discovery at its discovery date to provide better granularity and representation of discovery timing to better understand a play’s maturity and better predict future discovery sizes.

Ternary Plots

Use the Ternary Plot to explore variables comprised of the three components to understand each component’s contribution to the total. For example, easily visualize shales’ quartz, clay and carbonate percentages to compare different shale’s productivity based on fracturing and stimulation viability.

With limited data, a lognormal curve with the mean matching the sample average, the curve’s P10:P90 ratio can be modified to match analog data and fit an envelope of lowside and highside curves encompassing all the data values to represent the uncertainty of the mean.

Chance of Success and Dry Hole Analysis

Several handy tools are included for evaluating the predicted chance of success and dry hole reason for failure analysis of historical drilling results. .

Gain insight into the uncertainty of chance based on the historical number of successful and failed wells for a play as represented by a beta distribution. The chance uncertainty is greater with fewer wells with the ranged distribution narrowing as more wells are included.

Understand and model the Gambler’s Ruin probability (all wells are dry) and the likelihood of 1, 2, 3 or more discoveries given an average chance of success for a drilling program.

QC of the Prospect Inventory

Extract key rock volume and hydrocarbon yield data for a multitude of prospects to inspect and reality check the estimates used to calculate the volumetrics. Quickly identifying potentially biased estimates ensures consistency and helps removes any biases from the prospect database Crossplots of any key quantitative and qualitative attributes for multiple prospects can be charted to identify more favorable opportunities and outliers.