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Liquid loading of oil and gas wells occurs when the reservoir fails to deliver hydrocarbons to the surface due to accumulation of liquid in the well. In Oil reservoirs with gas cap and aquifer, liquid loading post shutdown revival of oil wells with high gas liquid ratio (GLR) is poorly understood and documented. The mechanism and prediction of onset of such liquid loading has been studied and discussed in this paper. The field observation of loading of high GLR oil wells could not be explained with steady state analysis in a mature carbonate offshore field. Transient analysis successfully revealed liquid loading as observed in field. However, correct set up of initial conditions is imperative for prediction using transient analysis method which is discussed in this paper. A workflow has been developed to predict onset of start-up liquid loading and to build in risking using a risk matrix incorporating relevant well parameters. The methodology was used to estimate the gas lift requirement and associated risked reserves. The liquid loading of high GLR oil wells after a brief period of shutdown has been successfully modelled using transient method by representing correct initial conditions. However, ability of well to re-deliver gas at the steady state GLR value during the start-up is an uncertainty. Hence, an element of risking has been captured to predict the most likely time of start-up loading using a risking criterion based on individual well's flow parameters. The results from the above methodology of prediction and risking show that wells are susceptible to liquid loading much in advance as predicted from steady state workflows which matches with the field observed well performance. This has a significant impact on the lift gas flowrate and top side infrastructure required to sustain flow from these wells. The paper also discusses a strong business case to evaluate liquid loading from predicted parameters at field development planning stage using the proposed workflow to secure future well production and save on additional costs of retrofitting gas lift infrastructure.
Liquid loading of oil and gas wells occurs when the reservoir fails to deliver hydrocarbons to the surface due to accumulation of liquid in the well. In Oil reservoirs with gas cap and aquifer, liquid loading post shutdown revival of oil wells with high gas liquid ratio (GLR) is poorly understood and documented. The mechanism and prediction of onset of such liquid loading has been studied and discussed in this paper. The field observation of loading of high GLR oil wells could not be explained with steady state analysis in a mature carbonate offshore field. Transient analysis successfully revealed liquid loading as observed in field. However, correct set up of initial conditions is imperative for prediction using transient analysis method which is discussed in this paper. A workflow has been developed to predict onset of start-up liquid loading and to build in risking using a risk matrix incorporating relevant well parameters. The methodology was used to estimate the gas lift requirement and associated risked reserves. The liquid loading of high GLR oil wells after a brief period of shutdown has been successfully modelled using transient method by representing correct initial conditions. However, ability of well to re-deliver gas at the steady state GLR value during the start-up is an uncertainty. Hence, an element of risking has been captured to predict the most likely time of start-up loading using a risking criterion based on individual well's flow parameters. The results from the above methodology of prediction and risking show that wells are susceptible to liquid loading much in advance as predicted from steady state workflows which matches with the field observed well performance. This has a significant impact on the lift gas flowrate and top side infrastructure required to sustain flow from these wells. The paper also discusses a strong business case to evaluate liquid loading from predicted parameters at field development planning stage using the proposed workflow to secure future well production and save on additional costs of retrofitting gas lift infrastructure.
Calcite scaling is a major production challenge in many mature oil fields. One of the most effective manners to control calcite scaling losses is to predict the scaling behavior in the wells using thermodynamic models and then use prediction results to build an operational program to control scaling in the field using a combination of effective acid washes and scale inhibitor (SI) injection. This paper presents a case study from a mature oil field and details the operational facets of scale prediction and control program. It describes both operational challenges as well as cost optimization involved in program implementation in the field. There are three major aspects which are discussed in this paper. First part deals with selection of right scale inhibition chemical for a field. It details laboratory experiment that was used to select the most appropriate chemical for the field. Also, this section describes the hardware infrastructure that should be in place to ensure effective implementation. Next section deals with the trial of chemicals, selected from lab analysis, in actual field conditions. Impact of scale inhibitor in controlling scaling within the tubing and flowarm of the wells is discussed. Secondly, benchmarking of new scale inhibitor chemical performance against the existing chemical is also presented in this section. This formed the technical basis for change in existing scale inhibitor and going for full fledged implementation in the field. Final part of the paper details field-wide implementation of scale inhibitor injection and chemical performance monitoring program. Critical parameters for chemical performance monitoring and field data on chemical dosage optimization using these critical parameters are presented. This section also presents several case studies showcasing impact of scale inhibitor injection on the well performance. This scale management approach has not only helped in terms of reducing production losses but also assisted in improving safety performance. Field data, highlighting the impact of scale inhibitor in significantly reducing the scaling in the wells, is shared in this paper. Post implementation of this program, asset has been able to reduce its scaling related production losses significantly (~90% reduction in less than 2 years). Once an oil field enters decline phase of its life cycle, reservoir pressure drops and water cut increases – conditions which favor high scaling behavior in the wells. Thus, scaling related losses presents a serious challenge in controlling production decline in a mature oil field. By adopting the operational practices shared in this paper, many mature oil field can benefit by reducing scaling related losses.
Carbonate and sulphide scales can form in CO2 and/or H2S-rich environments in a process which we refer to as "auto-scaling", i.e. these scales form in the produced brine due to a change in conditions such as pressure and temperature, not due to brine mixing. Particularly in production systems, carbonate and sulphide scales can form due to the evolution of CO2 and H2S from the aqueous phase to the gas phase caused by a pressure decrease. Carbonate scale formation in this manner is broadly understood; however, there are details of precisely how this occurs in auto-scaling processes which are not widely appreciated. Measuring the water composition at surface locations (e.g. at the separator) does not give a full indication per se of the amount of scale that has precipitated upstream of the sampling point. However, the composition of the water before precipitation occurs is required for predicting the scaling potential of the system, and this information is seldom available. In this paper, we propose a model that accounts for this issue, and that accurately calculates the carbonate and sulphide scaling profiles in CO2 and/or H2S-rich production systems by knowing only commonly available surface data – i.e. pressure, temperature, and fluid compositions (water, gas, and oil). A rigorous workflow which can do this calculation using any aqueous scale prediction model along with a PVT Model has already been published by the authors (Verri et al, 2017a). The current paper describes a new model to do these calculations which also includes an approach for estimating both the "correct" scaling case within a range of cases up to the "worst case" carbonate scaling scenario. A scale prediction model has been developed to include a three-phase flash algorithm (using the Peng-Robinson Equation of State) coupled with an aqueous electrolyte model (using the Pitzer equations as the activity model). This model is used to run a demonstration example showing the procedure to calculate accurate auto-scaling profiles in CO2 and/or H2S-rich production systems, which is based on building a sensitivity analysis on the ions directly involved in precipitation reactions. We also note that auto-scaling profiles in production systems are commonly obtained by sectioning the production system – either by parameterising depth with pressure and temperature, or by selecting specific locations (e.g. DHSV, wellhead, etc.). Then, established guidelines to treat scale (or not) based on the calculated saturation ratios and precipitated masses of scale can be applied. We show that such an approach is not optimal and that it can lead to under or over-estimation of scale treatments. Furthermore, building on our previous method (Verri et al 2017a) we propose an approach to model the cumulative amount of scale formed under full equilibrium conditions, which is not dependent on how the production system is sectioned. By doing so, the correct amount of scale formed in the production system is always calculated, thus avoiding non-optimum scale treatments. Our approach focuses on calculating the correct auto-scaling profiles in CO2 and/or H2S-rich production systems, and on correctly interpreting the results obtained by thermodynamic modelling and it can be easily integrated with commonly available scale prediction software.
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