APT: Innovative Formation Evaluation

APT: innovative formation evaluation

APT has been designed to address the highly nonlinear nature of geological data and is based on the geological differential method (GDM)1. It integrates various forms of data, preserves the geological identity and produces a true point by point solution of the target parameter. APT produces a true solution in comparison to the statistical methods which can only produce a realisation.

Features

  • Holistic approach, integrating data of various forms
  • Point by point best solution of the target parameter
  • Knowledge extraction from the data
  • Defines complex interrelationships between the various parameters
  • A data driven solution process
  • Does not impose any weighting on input parameters
  • Does not impose any empirical formulations on the data.

Applications

  • Predicts formation properties from mudlogs & drilling data
  • Incorporate core data directly in the calculation of formation properties
  • Predict missing logs
  • Log repair and log reconstruction
  • Pptimization of logging program.

APT Model representation:

APT maintains the data identity and character throughout the solution process, in contrast to other methods. APT preserves the geological characteristics for each and every lithology along the borehole as illustrated.
The same data is organised in two different ways to predict porosity in a prediction well far a field from the model well.

Case 1 – Data is organised with increasing depth.
Case 2 – Data is organised with increasing porosity.

APT predictions for both cases are identical as shown below:

 
case-studies
 

Case Study: Predict porosity & water saturation without wireline logs

Project challenges
Predict porosity and water saturation throughout the overburden: in the absence of wireline or logging while drilling (LWD) data by maximising the use of available mud-gas, drilling and Gamma Ray data.
 
Cases
 
Software solution

  • Enables overburden petrophysical evaluation without wireline and LWD data
  • Predicts Phi (Porosity ) and SW (water saturation) from the available gamma ray (GR), drilling and GWD data
  • Builds models using multiple wells with wireline derived PHIE and SW
  • Predicts logs in multiple target wells without wireline logs.