SIMPLIFIED PREDICTIVE MODELS ON SUSTAINABLE LOCAL CONTENT POLICY, AND HUMAN CAPACITY DEVELOPMENTS, IN NIGERIA’S OIL AND GAS INDUSTRY.

Abstract

Simplified predictive models developed to determine sustainable local content policy and human capacity development in the Nigeria’s oil and gas industry was carried out using secondary data from the Petroleum Technology Development Fund (PTDF) within ten years. The research was formulated on both descriptive and analytical statistical methods for the prediction of the expected capacity development value at optimum conditions based on critical industry need assessment and audit report between 2018 -2023 - a baseline study and the secondary data sets from PTDF trio-capacity development strategies from 2011 – 2021. From equations 2.1 – 2.11, it was established that challenging weaknesses could be turned into opportunities for the Fund. A comparative analysis of the Skills Gap Audit (SGA) and Simplified Predictive Models (SPM) was carried out using arithmetic mean, standard deviation and correlation coefficient from the assumed mean of the unclassified data. Tables 2.1 - 4.5 and Figures 2.1 and 2.6 are reference commonality data sets. The results established that more key performance indicators were captured in the SPM models (mean of 23.04 and deviation of 0.4) against the imputed values in the SGA, (mean 4.52 and deviation of 0.5) respectively. This research is useful for policy formulation and decision-making on local content policy formulations and capacity development programmes in Nigeria. Keywords: Predictive models, Sustainable local content policy, Petroleum technology Development Fund.