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The electrical submersible pump (ESP) is an essential and critical component in most low-enthalpy geothermal wells where high volumes of hot (up to 120°C) and harsh geothermal brine is required to be transported to the surface. Despite a great deal of knowledge and experience in the design and operation of ESP in the petroleum and water sector, reliability of geothermal ESPs requires further improvement. Frequent failures have been observed that resulted from sub-optimum design, installation and operation of these systems which made the lifetime of them shorter than the expected 5-7 years. In this paper we summarize the typical conditions in low-enthalpy geothermal systems (specifically in the Netherlands) and several observed reliability challenges. Lastly, we will discuss the gaps between the petroleum, water and geothermal practices and identify a list of R&D opportunities to better understand the geothermal ESP failures and improve ESP reliability. Testing ESPs in realistic geothermal conditions and a proper monitoring of the well-ESP system is crucial to improve the reliability of existing ESP designs and can enable the development of new geothermal ESP system designs.

Reliable forecasting of production rates from mature hydrocarbon fields is crucial both in optimizing their operation (via short-term forecasts) and in making reliable reserves estimations (via long-term forecasts). Several approaches may be employed for production forecasting from the industry standard decline curve analysis, to new technologies such as machine learning. The goal of this study is to assess the potential of utilizing deep learning and hybrid modelling approaches for production rate forecasting. Several methods were developed and assessed for both short-term and long-term forecasts, such as: first-principle physics-based approaches, decline curve analysis, deep learning models and hybrid models (which combine first-principle and deep learning models). These methods were tested on data from a variety of gas assets for different forecasting horizons, ranging from 6 weeks to several years. The results suggest that each model can be beneficial for production forecasting, depending on the complexity of the production behavior, the forecasting horizon and the availability and accuracy of the data used. The performances of both hybrid and physical models were dependent on the quality of the calibration (history matching) of the models employed. Deep learning models were found to be more accurate in capturing the dynamic effects observed during production – this was especially true for mature fields with frequent shut-ins and interventions. For long-term production forecasting, in some cases, the hybrid model produced a greater accuracy due to its consideration of the long-term reservoir depletion process provided by the incorporated material balance model.

The forecasting of gas production from mature gas wells, due to their complex end-of-life behaviour, is challenging and often associated with uncertainties (both measurements and modelling uncertainties). Yet, having good forecasts are crucial for operational decision making. In this paper, we present a purely black-box based approach, which combines the use of a data assimilation method, the Ensemble Kalman Filter (EnKF) and a modified deep LSTM model as the prediction model within the approach. This approach is tested on two mature gas wells in the North Sea which were suffering from salt precipitation. Results showed that the approach of combining a deep LSTM model within EnKF can be effective when deployed in a real-time production optimization environment. We observed that having the EnKF increases the robustness of the forecasts by the black box prediction model while reducing computational cost of retraining the black-box models for every individual well.

Liquid loading is a common problem in mature gas wells which due to low pressures in the reservoir, liquid phase cannot be lifted to the well head. In gas wells, foamer injection is one of the method to mitigate liquid loading and increase the stable gas production. In this study a modified version of the Bikerman gas sparging setup was designed to evaluate the foamers performance at conditions which are more representative to the ones in fields, such as higher pressures and temperatures. The effect of different parameters on foam performance is studied. The study covers the effect of pressure, temperature, hydrocarbon fraction and foamer agitation velocity on the foam performance. Three tests were performed to quantify the foamer performance; foam buildup, collapse and liquid carryover tests. It is shown that performing evaluation tests at temperatures and hydrocarbon fractions close to the field conditions is inevitable. These results could be used as a basis to improve the current foamer testing methods. The same procedure could be used for surfactants evaluation for Enhanced-oil recovery (EOR) application.

In mature gas wells which are suffering from liquid loading, foamer injection is employed to mitigate the liquid loading and increase the stable gas production. Evaluation of the foamers performance and their chemical stability prior to their injection in the wells is of great importance. For this purpose, laboratory tests are performed to give an indication on the foamers performance. In this study, we focus on the effect of hydrocarbon fractions (condensate/brine ratio) and temperature (up to 120 °C) on the foamer performance in laboratory tests. A modified version of the Bikerman gas sparging setup was designed to allow for tests at higher pressures (up to 15 barg) and temperatures (up to 150 °C), compared to the currently available test setups. Three tests were performed to quantify the foamer performance; foam buildup, collapse and liquid carryover tests. The results indicated that higher hydrocarbon fraction significantly reduces the foam formation rate and liquid unloading capabilities. The current work highlights the importance of using a representative brine-hydrocarbon ratio for foamer evaluation tests. Additionally, on the effect of temperature, three different foamers were studied and the results showed a significant negative influence of high temperature on the foamer performance. It was observed that the effect of temperature on foamer performance is surfactant dependent. Therefore, it is essential to evaluate foamers in the laboratory tests at the temperatures as close as possible to the field (downhole) temperature.

The skin friction factor f in a turbulent wall-bounded flow can be greatly reduced by using polymer solutions. In this paper we discuss experimental results on the effect of the Coriolis force on turbulent drag reduction. To study this, a horizontal smooth-walled pipe with internal diameter 25 mm is placed on a horizontal table rotating about its vertical axis. The rotation is made non-dimensional with friction velocity and pipe diameter, to form the Rotation number Ro. For a range of bulk Rotation number (Rob) between 0 and 0.6 for two different Reynolds numbers (Reb = 15 & 30 × 103), the pressure drop is measured, from which the average friction factor f is obtained. Additionally the effect of four different polymer concentrations has been investigated. The single-phase results show that the friction factor increases monotonic but gradual with Rotation. With polymer additives a drag reduction is found that increases with concentration, but which is not affected by the rotation.

In this research, long-term (up to 1000 years) geochemical modeling of subsurface CO2 storage was carried out on Permian Rotliegend reservoirs of depleted gas fields in northeast Netherlands. The results showed that mineral dissolution/precipitation has a minor effect on reservoir porosity. In order to validate this, we focused on the reactive surface area of minerals which we measured by scanning electron microscopy. These calculations resulted a range of surface area values for each mineral which were subsequently subjected to a parameter analysis and Monte Carlo uncertainty analysis. These analyses revealed that in the Rotliegend sandstones, the surface area of quartz has by far the largest effect on SMCO2 (total amount of CO2 sequestered as mineral). Mineral trapping of CO2 increased significantly with increasing quartz surface area. This leads to the conclusion that CO2 injection in a sandstone reservoir with fine grained quartz has a higher potential for mineral trapping of CO2. In addition, using parameter analysis we also could determine the effect of surface area of each mineral on its own dissolution/precipitation mechanisms as well as on the other minerals. For example, our results showed that dawsonite precipitation is proportional to kaolinite and K-Feldspar surface area.