The drift velocity of a gas bubble penetrating into a stagnant liquid is investigated experimentally in this paper. It is part of the translational slug velocity. The existing equations for the drift velocity are either developed by using the results of Benjamin (1968) analysis assuming inviscid fluid flow or correlated using air/water data. Effects of surface tension and viscosity usually are neglected. However, the drift velocity is expected to be affected by high oil viscosity. In this study, the work of Gokcal et al. (2009) has been extended for different pipe diameters and viscosity range. The effects of high oil viscosity and pipe diameter on drift velocity for horizontal and upward-inclined pipes are investigated. The experiments are performed on a flow loop with a test section with 50.8-, 76.2-, and 152.4-mm inside diameter (ID) for inclination angles of 0 to 90°. Water and viscous oil are used as test fluids. New correlation for drift velocity in horizontal pipes of different diameters and liquid viscosities is developed on the basis of experimental data. A new drift-velocity model/approach are proposed for high oil viscosity, valid for inclined pipes inclined from horizontal to vertical. The proposed comprehensive closure relationships are expected to improve the performance of two-phase-flow models for high-viscosity oils in the slug flow regime.

Forecasting of production in unconventional prospects has gained a lot of attention in the recent years. The key challenges in unconventional reservoirs have been the requirement to put online a) a large number of wells in a short period of time, b) well productivity significantly driven by completion characteristics and that c) the physics of fluid flow in these prospects still remain uncertain. In this paper, machine learning algorithms are used to forecast production for existing and new wells in unconventional assets using inputs like geological maps, production history, pressure data and operational constraints. One of the most popular Machine Learning methods – Artificial Neural Network (ANN) is employed for this purpose. ANN can learn from large volume of data points without assuming a predetermined model and can adapt to newer data as and when it becomes available. The workflow involves using these data sets to train and optimize the ANN model which, subsequently, is used to predict the well production performance of both existing wells using their own history and new wells by using the history of nearby wells which were drilled in analogous geological locations. The proposed technique requires users to do less data conditioning and model building and focus more on analyzing what-if scenarios and determining the well performance.

Production forecasting in shale reservoirs is a challenging task because of the complex influences of geology, lithology, stimulation practices, etc. The large well count makes history matching and forward simulation particularly time consuming and laborious. In such a context, it is important to consider alternative methods, and to this end, we have developed two new methods of forecasting production. The first method uses data mining techniques, which allow the analysis of large quantities of data to discover meaningful pattern and relationships. These can subsequently be used for prediction. Some common data mining tools are neural networks (NN), genetic algorithms (GA), and self-organizing maps (SOM). Our method uses NN for predicting the future performance of a shale gas well based on historical production data of the previous year. The decline in production is captured during the NN training process and applied to the production data during the forecasting phase. The model is simple, elegant and fast and is able to forecast production in an unconventional play with reasonable tolerance. The second method uses time series analysis. It the trend, changes in value, rate of decline, and correlation with the past to generate a rapid and accurate forecast. The stock markets use this technique, and it is safe to say that if it can predict the stock ticks, then it can yield good results on a fluctuating, but surely declining, production rate. These methods are elegant and fast and are able to forecast production in an unconventional play with reasonable tolerance. They are not data intensive and can also be automated to be applied to a large number of wells, which makes them particularly useful in integrated operations in which a comparison of actual versus predicted behavior would enable operators to quickly identify problem wells for a more detailed investigation. The methods were applied to wells from the Barnett, Bakken, and Eagle Ford plays.

The effect of downward and upward pipe inclinations on flow characteristics for high viscosity oil-gas two-phase flow was experimentally studied. 400 experimental tests were conducted in a 50.8-mm ID pipe for ±2° inclinations. Four different oil viscosities, namely, 0.585, 0.378, 0.257 and 0.181 Pas, were considered. Superficial gas and liquid velocity varied from 0.1 to 3.5 m/s and 0.1 to 0.8 m/s, respectively. Flow pattern and pressure drop are reported. The experimental results were used to evaluate different flow pattern maps, models and two-phase flow correlations.

The Oilfield is a cash flow dependent business, making the speed of decision-making very important. Our industry collects an incredible amount of data, but the volume of this data, coupled with inefficient oilfield software, slows down analysis and decision making. Critical decisions are delayed, and decision quality is affected. Smart Assistants like Siri and Alexa have become extraordinarily popular lately due to their ability to perform routine tasks and make information readily accessible. Considering the oil industry's drive towards enhanced speed and efficiency, there is a considerable upside in applying the concept of a Smart Assistant to oil and gas applications. This Smart Assistant for Oil and Gas would consolidate massive amounts of data, derive knowledge from it, automate workflows, and provide faster and consistent insights. This paper will look closely at a specific application of this Smart Assistant: analyzing flowback data from newly drilled wells. Engineers at Equinor have recently leveraged learnings from this Smart Assistant to increase efficiency in day-to-day tasks and realize results.