The most important transition of the energy matrix in Peru was characterized by an economic bonanza between the years 2009 - 2011 and, whose energy intensity (I.E) was reflected by the accelerated growth of the GDP; which, caused an exponential increase in the energy demand of Peru and, whose multiplying effect was produced by the energy exchange of the predominance of Hydroelectric Energy towards the development of Natural Gas with the Camisea Gas Megaproject, however, it was not considered the impact of other factors. In this sense, the present study requires contextualizing the Energy Trilemma: 1) the country's energy security; through an energy efficiency policy in response to meeting demand in line with GDP growth, 2) energy equity, for the access of quality energy and accessible prices to more vulnerable populations with a diversified energy matrix; 3) environmental sustainability, to describe the Environmental Commitments of Peru with the COP24. The methodology is based on a macroeconomic-energetic model, whose architecture begins with historical information between the years 1970-2016 with respect to GDP vs. primary energy consumption; to then calculate the annual energy intensity of Peru and its CO2 emission according to the polluting factor of each primary matter. Followed, using projections of the GDP from 2017 to the year 2035 (3.8% per year - Conservative case with information from the World Bank) and 3 scenarios of decrease in energy intensity of 1%, 1.5% and 2% per year, may increase energy efficiency and reduce the emission of CO2 in the proportion of 10.4%, 15.2% and 19.6% respectively between 2017-2035. As a result, the total energy consumption will be estimated up to the year 2035 in Millions of TOE, according to each scenario of variation in energy intensity (ΔI. E). and with the forecasts in the distribution of the energy matrix of the years 2025 and 2035 through the BAU methodology, its forecasts of each primary material will be known (Natural Gas, Oil, Coal, Hydroelectric, Renewable Energy, others) until the year 2035 This will allow us to know the forecast of CO2 emissions based on each pollution factor of the primary sources and energy intensity levels predicted with respect to the base case (Δ IE = 0). Finally, Peru's commitment to COP24 will be evaluated based on the per capita correlation of the country / world population in the estimation of the cumulative maximum emission limit of 2.13 GtCO2 between 2017-2035 for Peru, if the temperature is not increased more than 2 ° C for the year 2100 and guarantee the demand of Peru under an optimized Energy Trilemma.

Management and portfolio evaluations is one of the crucial tasks that accurately account for valuating a petroleum asset. Usually, variations in input parameters and uncertainty on the evaluation of oil assets affect those processes, as it has been occurring with the price of oil in the last year. This paper presents a new systematic process for the evaluation of portfolios of oil and gas fields where the performance and economic value of an entire portfolio decrease rapidly. The automated cash flow-curve analysis tool presented here uses an event detection algorithm combined with the design of quantile regression technique to provide a robust probabilistic estimate of future PDP (proved-developedproducing) reserves on a well-by-well basis. Individual well behaviors then aggregate stochastically to provide expected field and portfolio declines, with uncertainty ranges. Future well trends are estimated using probabilistic type-curves computed by data mining algorithms with a high-level of granularity.Most discussions and publications to date have centered on the methods to perform portfolio optimization. However, very little emphasis was put on using analytical and data mining methods to evaluate faster the status of a given portfolio. This paper will focus on the use of data mining related methods in analyzing oil assets/engineering data, identifying correlations, development of industry-based algorithms and in the determination of relationships that influence root cause and consequence of failure in mature fields.This paper proposes a new approach for mature fields. It includes models with technical Data Mining reserves estimation (either applied for volumetric calculations or performance data based in technical of Extracting hidden knowledge), an scenario matrix to account for the risk expressed in expectation curves, an estimation of the "Pseudo-limiting" risk and value terms, and defining a hierarchy of portfolio with data mining technique. Furthermore, it proposes a methodology of life-cycle assessment and surveillance of reserves estimation by integrating of the "Pseudo-limiting" risk and the ratio expected value to capital investment under the concepts of risk management.The developed models showed good performance with minimal prediction errors. These results are promising, lending credence to the application of computational Intelligence for even more complex reservoir systems. They should also boost confidence in the use of advanced well structures for field applications.The consideration of such needs bring up the following purposes:Graph Nº 14 -Operating costs (OPEX) vs Timing (Years).Graph Nº 15-Project 3EX -Vopex Curve.Graph Nº 16 -Project 3EX -Vopex Curve considering function Log. OTC-26104-MSGraph Nº 17-Investments (Capex) vs Timing.Graph Nº 18 -Oil Price Volatility -2015.

Tight gas reservoir has potential to provide a significant contribution to meet the global energy demand. Unconventional resource plays and in particular tight gas reservoir are generally characterized by lower geologic risk but higher commercial risk. For that reason, a precise understanding of the potential range can lead to the commercial success; this weighs on the economic evaluation process. The cutting-edge method "Technical Datamining" (DM), use artificial intelligence, statists, and algorithm of learning machines to accomplish new knowledge of clustering and predictive types. Neural networks-DM are computational models that have been used in different research fields with outstanding results. Thus, models of temporal series are pursued to develop to achieve reliable estimations of the main economic indexes: NPV, IRR, Payout and investment performance in the high-risk Oil & Gas portfolios, in particular economic evaluation of unconventional/Tight Gas resources, which is our concern. Neural networks learn from experience and errors: when more wells of the investment's portfolios are added, the experience will improve. The process of knowledge improvement begins with the extraction, transformation and loading data to the collection of the resultant model and its analysis. This involves an exhaustive work with the exploration and evaluation with the behavior of independent variables (Capex, Opex, Reserves, Gas Price and Time), the outliers, the normalization, variability and the distributions. Furthermore, it is vital to maintain a complex and extensive training of the neural network model with different parameters and iterations, using the previous experience's expert. Our study has 4 years and a monthly seasonality for processing the data in the search to optimize decision making. The model application will be developed in the sectoral block of the Lajas Formation of the Neuquén Basin, with six wells in production, the GOIS value above 3000 MMm3 and the current recover factor estimated in 19 %. In addition to this, are expected the incorporation of new wells to the block to increase the recovery factor above 35 % and thus improve the return on investment (NPV / Investment). Finally, the construction of neural network model will provide predictive values more precisely through a time series using 80 % focusing on tasks for training and 20% for testing, with minor errors of 5 %. Extracting hidden knowledge or information not trivial of dataset to be used in making decision. Discovery of unknown models [1][2] in order to discover meaningful patterns and rules [3].