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Down-Hole Flow Monitoring with Distributed Sensing Systems
Dennis Dria, Myden Energy Consulting, PLLC The devil is, indeed, in the (interpretation) details. Presented here is an explanation of one robust interpretation method along with a brief description of the validation of using the appropriate model with distributed temperature sensing data to calculate zonal production rates.

Subsea well architecture and infrastructure constraints frequently make traditional production logging cost prohibitive as well as operationally challenging and risky. The drive towards permanently installed down-hole monitoring systems has opened doors for proper well and reservoir surveillance in this operating environment, providing the subsea producer options to acquire much-needed information about which hydrocarbon formations are living up to their potential with regards to rates as well as direct indication of reservoir performance such as zonal decline rate, location of water breakthrough and formation impairment/skin increase.
Learnings from onshore and offshore dry-tree down-hole monitoring experiences demonstrate the value of the data that comes from these types of sensing systems …but also demonstrate the shortcomings of not having robust inflow interpretation methods, a sure path to disappointment when managing expectations. Data from permanently installed sensors, as well as from production logging tools, frequently do not indicate directly or intuitively the relative production contribution from each producing zone. There are, however, several robust interpretation software suites which can provide quantitative answers to the inflow determination [e.g.: Donovan, et al., 2008; Brownet al., 2005].
The use of probabilistic or inversion-type interpretation methods offers a huge advantage when dealing with all types of production logging data, whether it be acquired in a traditional sense via wireline/CT/tractored interventions or from such permanently installed monitoring systems as multiple pressure gauges and fiber-optic or electronic distributed temperature sensors (DTS) [see Note 1 below]. These methods work by entering the interpretation with an initial set of values for the multiphase hold ups and flow rates. A weighted error function (see the Note 2 section at the end of this article) is generated by comparing the actual data for each “logging tool” (density, T, spinner, …) with modeled or theoretical tool responses calculated by forward simulation using robust multiphase flow and thermodynamic models, constrained with full enthalpy and material balance conditions. Through an automated optimization, the hold ups and flow rates are then variedso as to minimize the error function, resulting in a final answer that best honors all the data and constraints, including surface rates and estimates of A further strength of this inversion methodis that it allows the use of more than one tool measuring the same parameter (stochastic or direct-calculation methods would fail under this attempt as the system becomes mathematically over constrained). In the following example, velocity is calculated using the spinner as well as the temperaturelog. As it turns out, the ability to do this also provides benefits since the temperature log is particularly sensitive to zones that are producing water.
Application of such a method to a multizone gas well demonstrates the method’s potential value (Donovan, et al., 2008). A vertical well with over 20 independently producing intervals was logged with a traditional production logging tool suite to evaluate the completion effectiveness of each producing zone and to gather baseline gas inflow productivity to later determine zonal depletion rates, as well as identify which zones contributed significantly to water production.
The reason that this data set was chosen for the validation study was to compare the results of the temperature-only interpretation (viz., a “pseudo-DTS”) to that using all the production logging tools. This enabled a robust comparison between inflow interpretation using distributed temperature data, as one would have with a fiber-optic DTS or a distributed temperature array system, and traditional production logging tools.
The well in this example was vertical and completed by hydraulically fracturing22 stages with nearly a hundred perf sets over a vertical interval of almost 6,ooo feet, and was producing approximately 103 BWPD water and 4200 MCFD gas when data was acquired. The interpretation, using the inversion/optimization method described above, indicated the location and amounts of gas and water influx, shown in the panels designated as “T + Full PL” in the figure. The interpretation using the temperature and a few pressure points simulated a distributed temperature sensing (DTS) data set along with down-hole pressure gauges. These “pseudo-DTS” results are shown in the panels designated “T & P only”. One observes that the water entry locations and relative amounts are virtually identical for the two interpretations. The gas inflow distribution using DTS only shows remarkable agreement with the results obtained using all of the production logging tools. That the two answers do not agree perfectly is to be expected, as the temperature-only data set represents a mathematicallyunderconstrainedproblem and uses a significantly smaller amount of independent data than is available with the full production logging data set. Also, there is slightly reduced sensitivity in the DTS results, primarily at the shallower depths of the well. This is explained by the “dilution” of the temperature changes caused by the fluid influx: production volumes from shallower zones are mixed with much larger volumes of produced fluids being carried up the production string.
In summary, one has seen that using a robust, physically accurate and consistent interpretation model can provide accurate results, for data from both traditional production logs as well as from sensors like permanently installed distributed temperature monitoring systems. Having such methods available and using them on data collected from permanently installed sensing systems at appropriate time intervals can provide a real-time, on-demand inflow profile for zonal production allocation and producing interval decline analysis. These methods, along with commercially available and evolving subsea down-hole distributed monitoring, open the door to effective production surveillance in the subsea operating environment.

Note 1
An installation of a permanent down-hole monitoring system is shown in pic 2. A bundle of 10 optical fibers, encased in a 6.4 mm OD stainless steel tube, is being clamped to the outside of production tubing as it is being installed in a production well. Clamps are used to bind the cable to the tubing, as well as protect the cable as it passes over the tubing couplings. The fibers within the stainless steel cable are used for distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) over the entire length of the well completion, and to provide communications with a permanent down-hole pressure gauge (PDHG). Data from these sensors are analyzed with such software as described above. Installation techniques similar to this are utilized for distributed electronic sensors such as electronic temperature and pressure arrays. The alternative to this down-hole monitoring systems is to enter the well during a one-off logging with a production logging tool string, typically 40 mm in diameter and up to 30 m in length. This process requires special pressure-control and usually has the well production shut in or significantly choked back.

Note 2 The error function to be minimized is the Chi-Squared function, where Yi,M is the observed data value (the measured log value) for tool i, Yi,E is the theoretical or modeled value for tool i, M is a user-chosen weighting factor and is the uncertainty of observed logging tool i (typically chosen as one standard deviation).