Downhole conditions of oil and gas reservoirs change with time. Monitoring this change is critical to enhance hydrocarbon recovery from the reservoir. Conventionally, downhole measurements of physical and chemical properties of downhole formation fluids are taken using wireline logging or using permanent downhole sensors. Wireline logging is a complex operation that requires several miles of wireline cable, a winch, a crane and a specialized crew that knows how to operate this equipment [1]. In addition, a blowout preventer, a lubricator and a specialized crew to install and operate are needed. The complexity and cost of wireline operations makes it difficult to acquire reservoir data frequently. The other alternative for gathering downhole data more frequently is installing permanent sensors or optical fibers in the well [2-3]. However, due to the harsh downhole conditions, these sensors need to be extremely reliable and continuously maintained. Moreover, surface data acquisition systems for these sensors increase their cost significantly and reduces their applicability to every well. Both methods have their own limitations and there is a need for more practical and less expensive oil field instruments for well logging. Engineering small, inexpensive and robust logging instruments has its own challenges. Solving these challenges has been the main focus area of Sensors Development Team at Aramco Research Center in Houston. This paper describes one of the concept ideas that was successfully engineered and tested in our facility and successfully deployed and retrieved in the field in Saudi Arabia in collaboration with our EXPEC ARC and Northern Area Production Engineering and Well Services Divisions in Saudi Arabia.
The stimulation of multilateral wells with Coiled Tubing (CT) has always imposed significant challenges to the oilfield. Starting with lateral's access, extended reach coverage, and finishing off with an adequate stimulation fluid placement to ensure treating all targeted zones. This paper presents an engineering approach that enables access to a multilateral open-hole completion and evaluates fluid placement using the Distributed Temperature Sensing (DTS). The through-tubing multilateral access tool has been designed and deployed on a CT string, including a hybrid fiber optic and an electric cable connected to an intelligent Bottom-Hole Assembly (BHA) with multiple downhole sensors. The casing windows or open-hole junctions can be located with a precise real-time measurement of the differential pressure drop across the two downhole bottom-hole pressure sensors inside and outside the intelligent BHA. Moreover, the casing shoe and windows access will be immediately confirmed with the real-time Casing Collar Locator (CCL) signal loss. In contrast, the junction's access can be established just after a few tens of running footage thanks to the real-time inclination measurement from the accelerometer sub added to the BHA for the first time. The identification of access into the mother-bore was intuitively identified with the immediate loss of CCL signal at a depth of the casing shoe. The window localization was confirmed with a low drop in the downhole differential pressure at the intelligent bottom-hole assembly, which was not noticed at the surface. The deviation survey measured by the accelerometer sub showed a matching signature with the drilling deviation survey for both; the mother-bore and the lateral, which were successfully treated. Acquired DTS profile logs showed thought-provoking outputs. After applying the advanced interpretation algorithms, communication between the lateral and various heterogeneities in the formation was detected. The CT intelligent BHA deployment enabled the real-time downhole measurement of pressure drop, CCL, and inclination, allowing a quick confirmation of each lateral with confidence. It supersedes the previously used techniques by eliminating all limitations related to pressure monitoring at the surface and the requirement to tag different measured depths for each lateral. Various conclusions were driven, which allowed re-building operational procedures to improve the matrix stimulation treatments in offset wells. Several domains were integrated to create a fit-for-purpose solution for a complex operation. Joint efforts including stratigraphy, fluids science, and well intervention technologies could yield a proven algorithm to be applied.
Stabilization time is an essential key for pressure measurement accuracy. Obtaining representative pressure points in build-up tests for pressure-sensitive reservoirs is driven by optimizing stabilization time. An artificial intelligence technique was used in the study for testing pressure-sensitive reservoirs using measuring gauges. The stabilization time function of reservoir characteristics is generally calculated using the diffusivity equation where rock and fluid properties are honored. The artificial neural network (ANN) technique will be used to predict the stabilization time and optimize it using readily available and known inputs or parameters. The values obtained from the formula known as the diffusion formula and the ANN technique are then compared against the actual values measured from pressure gauges in the reservoirs. The optimization of the number of datasets required to be fed to the network to allow for coverage over the whole range is essential as opposed to the clustering of the datasets. A total of about 3000 pressure derivative samples from the wells were used in the testing, training, and validation of the ANN. The datasets are optimized by dividing them into three fractional parts, and the number optimized through monitoring the ANN performance. The optimization of the stabilization time is essential and leads to the improvement of the ANN learning process. The sensitivity analysis proves that the use of the formula and ANN technique, compared to actual datasets, is better since, in the formula and ANN technique, the time was optimized with an average absolute relative error of 3.67%. The results are near the same, especially when the ANN technique undergoes testing using known and easily available parameters. Time optimization is essential since discreet points or datasets in the ANN technique and formula would not work, allowing ANN to work in situations of optimization. The study was expected to provide additional data and information, considering that stabilization time is essential in obtaining the pressure map representation. ANN is a superior technique and, through its superiority, allows for proper optimization of time as a parameter. Thus it can predict reservoir log data almost accurately. The method used in the study shows the importance of optimizing pressure stabilization time through reduction. The study results can, therefore, be applied in reservoir testing to achieve optimal results.
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