Prediction of Stimulation From Acid Fracturing Treatments Using Finite Fracture Conductivity
- E.J. Novotny (Exxon Production Research Co.)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- September 1977
- Document Type
- Journal Paper
- 1,186 - 1,194
- 1977. Society of Petroleum Engineers
- 2.5.2 Fracturing Materials (Fluids, Proppant), 3 Production and Well Operations, 5.3.2 Multiphase Flow, 4.1.2 Separation and Treating, 4.2.3 Materials and Corrosion, 5.8.7 Carbonate Reservoir, 2.2.3 Fluid Loss Control, 5.2 Reservoir Fluid Dynamics, 3.2.4 Acidising
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A method is presented for predicting stimulation ratios achieved from acid fracturing treatments of carbonate reservoirs. The method entails calculating acidized-fracture conductivities along the fracture from the acid-concentration profile and accounts for the amount of rock dissolved per unit area, the closure stress on the fracture, and the rock strength. per unit area, the closure stress on the fracture, and the rock strength. Introduction
Acid fracturing is a technique commonly used to stimulate carbonate reservoirs. Acid, or a fluid containing acid, is injected into a well at a rate and pressure sufficient to fracture (crack) the formation. The acid flows through the fracture and reacts with the carbonate rock, partially dissolving it. When the treatment ends and the partially dissolving it. When the treatment ends and the fracture closes, a conductive channel remains through which reservoir fluids can flow. The degree of improvement (stimulation) of production depends on the length and conductivity of the acidized fracture.
Recent research has led to more realistic modeling of acid fracturing treatments. However, these prior methods for predicting the stimulation resulting from an acid fracturing treatment do not include the effect of acidizedfracture conductivity. Rather, the fracture is assumed to be infinitely conductive to a distance from the wellbore where the acid is 10 percent of its initial concentration. This distance is usually referred to as the acid penetration distance. It will be shown that using acid penetration distance and infinite fracture conductivity is a good approximation for predicting stimulation from plain HCl fracturing treatments. However, assuming infinite conductivity gives much higher predicted stimulations for low fluid-loss, acid-external emulsions than are observed in the field.
This paper introduces a method for predicting the stimulation ratio resulting from an acid fracturing treatment using finite fracture conductivity. The amount of rock dissolved along the fracture is calculated from the acid concentration profile. From the amount of rock dissolved, the strength of the rock, and the closure stress on the fracture, realistic acidized-fracture conductivities at various fracture lengths are calculated using relationships developed from laboratory data. From these conductivities, stimulation ratios are predicted and show excellent agreement when compared with stimulations observed in the field. These comparisons include both plain HCl and low fluid-loss, acid-external emulsion plain HCl and low fluid-loss, acid-external emulsion treatments. This improved predictive method is then used to compare equal-cost 15-percent HCl, 28-percent HCl, and low fluid-loss acid emulsion fracturing treatments under various conditions. Finally, techniques for improving acid fracturing treatments are examined.
Results and Discussion
Comparison of Predicted With Observed Stimulations
Stimulation ratios predicted using the method detailed later in this paper showed excellent agreement when compared with observed stimulations from 31 field treatments. Data for these treatments are presented in Table 1, and Fig. 1 shows a plot of predicted and observed stimulation ratios. If predicted values were exactly equal to the observed stimulation ratios, the data points would all lie on the 45 deg. line. points would all lie on the 45 deg. line. The fit between theoretical model predictions and actual field observations was determined from a statistical analysis.
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