Abstract
In an era where cost is a significant component of decision making, every possibility of reducing operational cost in the Oil and Gas industry is a welcome development. The volatile nature of the Oil market creates uncertainty in the industry. One way to manage this uncertainty is by the ability to predict and optimize our operations to reduce all of our cost elements. When cost is planned and predicted as accurately as possible, the operation optimizations can be managed efficiently.
Practically, all new drills require CT unloading of the completion or kill fluids to allow the natural flow of the wells. Hitherto, there is no mathematical model that combines information from one of the wells in an unloading dual completion project that can be used to aid decision-making in the other well for the same unloading project and thereby result in an effective cost-saving. Deploying the mathematical model of cost element prediction and optimization can minimize operational unloading costs. The two strings of the dual completion flow from different reservoirs. Still, the link between the two drainages post completion is the kill fluid density, and can aid in cost estimation for optimum benefit. The lesson learned or data acquired from the lifting of the slave reservoir string can be optimized to effectively and efficiently lift the master reservoir string. The decision of first unloading the slave reservoir string is critical for correct prediction and optimization of the ultimate cost.
The mathematical model was able to predict the consumable cost elements such as the gallon of nitrogen and time that may be spent on the long string from the correlative analysis of the short string. The more energy is required for unloading the short string and it is the more critical well than the long string because it is the slave string since no consideration as such is given to it when beneficiating the kill fluid to target the long string reservoir pressure with a certain safety overbalance. The rule for the mud weight or the weight of the kill fluid is the highest depth with highest reservoir pressure which is the sand on the long string.
With the data from the short string and upper sand reservoir, the lift depth and unloading operation can be optimized to save cost. The short string will incur the higher cost and as such should be lifted last and the optimization can be done with the factor of the LS.
Peramalan Kinerja Lapangan SNP Dengan Injeksi Air Menggunakan Metode Material Balance
