Pickling well tubulars to prevent pumping unwanted materials into the formation is an issue that should be decided on an individual job basis. Acid pickling of tubing is a process of flow in a tube or annulus with heterogeneous reactions occurring at the wall of the tube. The reacting solid species at the wall are primarily mill scale, pipe dope, and other scales of various types. The fluids injected for pickling are usually strong acid solutions (HCl), but may also include surfactants, organic solvents, and gelled solutions to aid in lifting solids loosened from the tubing wall. Thus, the process is a complex one involving several reactions with perhaps multiple stages of fluids.
Tubing pickle is an essential part of well stimulation treatments if the main acid job is to be bullheaded. Standard design parameters for a pickle treatment depend on experience and personal judgment. It appears that the standard pickle treatment design is overestimating the required volume of pickling acid. Field data indicate that excessive acid volumes are used for tubing pickle because large returns of unreacted (live) acid are usually recovered on the surface. Careful analysis of flowback samples showed that only a fraction of the acid is being consumed by mill scale and other tubing contaminants.
In this paper, proposed mechanisms to explain the behavior of acid contact with the tubing are presented and a mathematical model for predicting acid consumption and dissolution of tubular contaminants is developed. The model is developed for the bull-heading case where the pickling acid is pumped down the tubing, then flowed back to the surface. The model considers the reaction of acid with mill scale (Fe3O4). The equations formulated are solved numerically to predict the concentrations of major chemical species as a function of position along the tubing and in the effluent from the well during flowback.
Such a model can be extremely valuable in optimizing the application of future pickling treatments. Using this model, the acid volume needed for pickling operations can be reduced significantly and other improvements can be made without extensive, costly field testing. Finally, some recommendations are made to design optimum pickling treatments.