The main challenge in acidizing multilayered carbonate reservoirs is to achieve optimum placement of the stimulation fluid. Optimum placement has often been interpreted as uniform placement of fluid across the producing interval, regardless of the objective of the intervention or its constraints. In addition, the current practice tends to design acid treatments based on information such as damage skin in each reservoir layer, which is rarely accurate. Therefore, a treatment initially designed as "optimal" often does not meet expectations because its design was based on erroneous formation and/or damage properties.

We present an alternative approach to designing and executing matrix stimulation treatments in multilayered carbonate reservoirs. The proposed workflow does not rely on damage skin by layer during the design and the execution phases of the treatment. It instead requires a step of determining where the acid must be injected, at which rate and volume, to meet an objective function. This determination can be done considering an undamaged well, therefore relying on formation properties that can be measured with sufficient accuracy. How damage influences fluid placement is dealt with during the execution phase without relying on indirect parameters such as skin or local skin. Instead, by measuring the rate and volume of fluid injected in each layer, one can take the corrective actions that will ensure the placement of the optimal volume of stimulating fluid in the target layer(s), at the optimal rate determined in the design step.

By using the objective function in conjunction with a fluid placement simulator, we show that different treatment objectives and/or constraints may require radically different strategies and tools for an optimal outcome. In addition, when carrying out a sensitivity analysis on parameters that are uncertain, it is possible to select the treatment that is most robust to variables that cannot be measured with accuracy (i.e., the treatment whose outcome is the least sensitive to uncertainties).

The workflow we present here allows decoupling the questions of optimal placement of a fluid for a given well, the performance of the materials selected, and the treatment schedule considered. By using the optimum design and the objective function as guidelines, engineers can now take full advantage of real-time monitoring capabilities of tools such as fiber-optic-enabled coiled tubing or slickline, not only to take corrective actions during the treatment, but also to ensure that optimum volumes and rates are effectively delivered in the targeted zones to meet the stimulation objective. Case studies will be presented to illustrate this novel approach.

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