Petroleum consumption increases around the world and production of conventional reservoirs can't cover the increased demand. So, producing unconventional resources is an imperative necessity.Unconventional resources are characterized by very low permeability. Drilling horizontal wells in these resources and completed them with multiple hydraulic fractures make the reservoir. Hydraulic fractures work as paths for hydrocarbon to flow toward the wellbore to achieve an economic production rate. Production behaviour of these wells is characterized by long-term transient flow followed by boundarydominated flow. Many decline curve analysis models have been developed to simulate this behaviour, but none of them can capture all flow-regime types. This paper reviewed the most popular and used decline curve analysis models: Arps model, power-law exponential model, stretched exponential production decline model, T-model, logistic growth model, Duong model, Yu-Miocevic model and extended exponential decline curve. This paper summarized the origins, derivations and assumptions of these eight models. This paper also presents a comparative study of these models using production data from unconventional gas and oil reservoirs. To facilitate conducting this study, the eight decline curve analysis models were programmed in a software application written in python language. This software application calibrated models' parameters to production data using trust region reflective algorithm. The value of estimated ultimate recovery predicted using this software application is consistent with that predicted using the linear flow analysis model. The comparative study can serve as a guideline for petroleum engineers to determine when to use each model.

Decline curve analysis (DCA) is one of the most common tools to estimate hydrocarbon reserves. Recently, many decline curve models have been developed for unconventional reservoirs because of the complex driving mechanisms and production systems of such resources. DCA is subjected to some uncertainties. These uncertainties are mainly related to the data size available for regression, the quality of the data, and the selected decline curve model/s to be used. In this research, first, 20 decline curve models were summarized. For each model, the four basic equations were completed analytically. Second, 16 decline curve models were used with different data sizes and then a machine learning (ML) algorithm was used to detect the outlier from shale gas production data with different thresholds of 10, 15, and 20%. After that, the 16 models were compared based on different data sizes and the three levels of data quality. The results showed differences among all models’ performances in the goodness of fitting and prediction reliability based on the data size. Also, some models are more sensitive to removing the outlier than others. For example, Duong and Wang’s models seemed to be less affected by removing the outlier compared to Weng, Hesieh, stretched exponential production decline (SEPD), logistic growth (LGM), and fractional decline curve (FDC) models. Further, the extended exponential decline curve analysis (EEDCA) and the hyperbolic–exponential hybrid decline (HEHD) models tended to underestimate the reserves, and by removing the outlier, they tended to be more underestimators. This work presented a comparative analysis among 16 different DCA models based on removing the outlier using ML. This may motivate researchers for further investigations to conclude which combination of the outlier removers and DCA models could be used to improve production forecasting and reserve estimation.

Gas compressibility factor is the most important gas property. Its value is required in many petroleum engineering calculations. There are many different sources of gas compressibility factor value such as experimental measurements, equations of state, charts, tables, intelligent approaches and empirical correlations methods. In absence of experimental measurements of gas compressibility factor values, it is necessary for the petroleum engineer to find an accurate, quick and reliable method for predicting these values. This study presents a new gas compressibility factor explicit empirical correlation for gas-condensate reservoir systems above dew point pressure. This new correlation is more robust, reliable and efficient than the previously published explicit empirical correlations. It is also in a simple mathematical form. The predicted value using this new correlation can be used as an initial value of implicit correlations to avoid huge number of iterations. This study also presents evaluation of the new and previously published explicit correlations.