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The key to successful planning of well interventions and other well actions is to understand the current state and the history of the well. Due to the large spread of telemetry systems with high-frequency (up to 1 measurement per second) measurement of parameters, it is possible to use machine learning methods for well events recognition. In this paper we consider well analysis with, aim to identify equipment failures and other influences affecting the behavior of wells. Typically, several parameters are recorded at the wellhead with high frequency: wellhead and bottom-hole pressure and temperature, flow line pressure and temperature. Also, readings of downhole measuring devices and well logs are periodically made and recorded. The readings of well parameters can be influenced by many factors: manual manipulations on the well, changes in the composition of the produced products, well integrity issues and others. This work suggests an approach that allows to identify and classify events at the well. The approach is based on the results of constructed synthetic dynamic models of wells and observation of the real behavior of wells. It allows to identify the behavior of individual measured parameters and classify events using all measured parameters in aggregate. The proposed algorithm allows retrospective analysis of data and identification of different events, such as well tests that occurred in the past. The algorithm also allows the analysis of incoming data and identification of well events in real time. Retrospective analysis of the data was useful not only for detecting anomalies and malfunctions, but also for building a real log of events at the well, monitoring well interventions and building reports on well performance. The analysis of event records demonstrates that only minor part of well events is normally captured in central databases. The developed algorithm for natural flowing wells can be easily extended to wells equipped with mechanized oil production systems. For example, for wells with a gaslift or ESP installation. The algorithm can be easily integrated into corporate monitoring systems as an auxiliary tool.

At the stage of field development planning the choice of the lift method largely affects the overall evaluation of the project efficiency. This effect is derived not only from different OPEX required for artificial lift as determined by the energy characteristics and equipment service life but also from optimal utilization of surface facilities. The paper describes the approach to selecting the lift method on the basis of simultaneous problem solving used to estimate the optimal production flow rates and OPEX for every method under study subject to complicating factors and CAPEX for surface infrastructure. The described approach is based on the analytical models for assessment of feasible flow rates and power consumption for various lifting mechanisms given the possible geology and process constraints during the full field life cycle. The main differences from the conventional approach to selecting the lift method consist in the following: no need for specialized software tools for selecting downhole pumping equipment; flexibility in case of significant changes in the well production conditions which are very typical of greenfields with hard-to-recover reserves; option for simultaneous solution to the problem of selecting the field depletion strategy and optimal production method by means of calculating the realistic bottom-hole pressures at the stage of flow simulations. The paper presents a case study from introduction of this approach to selecting the lift method for the wells of R. Trebs' oil field. The field is rather deep with productivity proven in carbonate reservoirs with mixed type of porosity (fractured-vuggy-porous) and low matrix permeability. The fracture intensity and vugginess are characterized by lateral heterogeneity. The application of the conventional approach to selecting the lift method with the help of ‘average' wells is problematic due to a wide scatter in future well productivity numbers. In this case the optimal producing method was selected not only for the individual wells but also for the full field during its life cycle. The selection of the production method was also based on the probable predicted problems typical for this field (paraffins, salts and corrosion) and cost evaluation for preventive measures. The modeled scenarios defined the best arrangements for acquisition and lease of equipment depending on the cost of the pumping units, construction of repair shops and logistics. The sensitivity analysis was run to account for the uncertainties in the source input data. The results of this analysis were presented as applicability maps for each of the methods on the basis of the key production parameters. The proposed approach was implemented in a computational module which may be utilized to select the optimal production method for wells in fields with complex geology and considerable variability in well behavior. The evaluation of the realistic or feasible project targets may be used to adjust the target production profiles in flow dynamic simulators.

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