Monitoring Multilayered Reservoir Pressures and GOR Changes Over Time Using Permanently Installed Distributed Temperature Measurements

Abstract

Abstract Early identification of differential depletion in stacked reservoir sands, before water or gas breaks through, is the key to optimal reservoir drainage. However, hitherto it has not been possible to monitor reservoir pressure changes in individual layers after a well has been put on production without installing an intelligent completion or performing a multirate inflow performance relationship (IPR) test. This paper describes a technique allowing individual layer pressures, or gas/oil ratios (GOR), to be monitored continuously during production. The technique employs the use of a rigorous near-well nodal reservoir pressure and thermal model to analyze permanently installed distributed temperature measurements. By modeling a range of typical flowing scenarios we demonstrate that distributed temperature measurements respond to changes in production caused by depletion in individual reservoir layers. We also show that in addition to flow rate determination, layer pressure changes smaller than 10 psi can be detected by changes in the measured temperature profile, as long as there is no breakthrough of gas or water. The model is also used to define the limits of the technique's operating envelope. Increases in the flowing layer GOR will decrease the layers' fluid viscosity, resulting in a change in flow rate together with a decrease in flowing fluid temperature due to the Joule-Thomson effect. Consequently, layers where the GOR increases, identifying early gas breakthrough or fingering, can also be detected using distributed temperature monitoring. The theoretical models are supported by real well examples, where the calculation of different layer pressures caused by depletion is confirmed by shutting in the well and observing the resulting crossflow with permanently installed distributed temperature monitoring. These models, along with continuously monitored temperature profiles, can be used to refine reservoir models and thus improve overall field recovery. Introduction There isno doubt that monitoring layer production and pressures is the key to optimal reservoir drainage in stacked reservoirs. Knowing which layers are depleting and at what rate is information that can be directly input into the reservoir model. Well tests, by their very nature, average the buildup pressure in a multilayered system. Therefore, if one wishes to measure individual layer pressures only two options are currently available. One can install an intelligent completion and shut-in each zone periodically to obtain a buildup pressure, which can be costly and results in loss of production during the shut-in. Alternatively one can perform a multirate IPR test using a production logging tool (PLT) log, which again results in loss of production because the well is often shut-in for some time and also needs to be flowed at a reduced rate. There is now a third alternative that does not require shutting in the well or reducing flow rate, and it can be performed any time during the life of the well. It is based on the fact that over time the individual layer flow contributions will change naturally as the reservoir layer pressure changes. Flow distribution is monitored using a permanently installed fiber-optic distributed temperature system (DTS). During early production, a near-wellbore reservoir model is characterized to match the well-bore temperature profile calculated from the thermal model to the early measured DTS data when reservoir layer pressures and other parameters are known (i.e. from logs). As the flow profile changes with time, the model can then be used to predict the reservoir pressures from the change in temperatures. Of course, this could be achieved by running a series of production logs during the life of the well; however, this option will require intervention and is not always practical. Further benefits of continuous monitoring using DTS systems are that if a particular layer's GOR increases, this can also be identified and evaluated using the DTS system.