No abstract

Shale gas reservoirs have huge amounts of reserves. Economically evaluating these reserves is challenging due to complex driving mechanisms, complex drilling and completion configurations, and the complexity of controlling the producing conditions. Decline Curve Analysis (DCA) is historically considered the easiest method for production prediction of unconventional reservoirs as it only requires production history. Besides uncertainties in selecting a suitable DCA model to match the production behavior of the shale gas wells, the production data are usually noisy because of the changing choke size used to control the bottom hole flowing pressure and the multiple shut-ins to remove the associated water. Removing this noise from the data is important for effective DCA prediction. In this study, 12 machine learning outlier detection algorithms were investigated to determine the one most suitable for improving the quality of production data. Five of them were found not suitable, as they remove complete portions of the production data rather than scattered data points. The other seven algorithms were deeply investigated, assuming that 20% of the production data are outliers. During the work, eight DCA models were studied and applied. Different recommendations were stated regarding their sensitivity to noise. The results showed that the clustered based outlier factor, k-nearest neighbor, and the angular based outlier factor algorithms are the most effective algorithms for improving the data quality for DCA, while the stochastic outlier selection and subspace outlier detection algorithms were found to be the least effective. Additionally, DCA models, such as the Arps, Duong, and Wang models, were found to be less sensitive to removing noise, even with different algorithms. Meanwhile, power law exponential, logistic growth model, and stretched exponent production decline models showed more sensitivity to removing the noise, with varying performance under different outlier-removal algorithms. This work introduces the best combination of DCA models and outlier-detection algorithms, which could be used to reduce the uncertainties related to production forecasting and reserve estimation of shale gas reservoirs.

No abstract

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.

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.