ENSURING ENVIRONMENTAL FRIENDLINESS OF HORIZONTAL. SHALE GAS
WELLS THROUGH ZONAL ISOLATION: A MODEL-. BASED APPROACH.
ENSURING ENVIRONMENTAL FRIENDLINESS OF HORIZONTAL SHALE GAS WELLS THROUGH ZONAL ISOLATION: A MODELBASED APPROACH Shyam Panjwani, Michael Nikolaou University of Houston, TX, USA
Introduction Shale gas horizontal wells face problems related to gas leakage from various zones of a well into the air and water reserves. Protecting the environment and improving well productivity is one of the biggest challenges for shale gas production. This problem can be solved by ensuring zonal isolation, namely avoidance of any fluids migration between rock formations penetrated by a well. Zonal isolation mainly depends on the quality of well cementing, namely the tightness of the seal created by the cement placed between the metal casing and well wall. Techniques used for direct quantification of cement bond quality include the cement bond log (CBL), variable density log (VDL) and segmented cement bond tool (SBT). VDL and SBT are designed for qualitative analysis, whereas CBL can provide quantitative estimation of cement bond quality in terms of the value of the bond index (BI), calculated using CBL data [1]. BI values close to zero indicate poor cementing while a value at 1 represents perfect cementing. Cement bond quality can be indirectly characterized as acceptable or unacceptable in terms of the sustained casing pressure (SCP), namely based on whether the well leaks or not at a pressure sustained above a certain threshold over a period of time. Cement bond quality is affected by several variables. The objective of this work is to build a data-driven predictive model that suggests values of these variables that ensure cementing of good quality, namely such that it provides reliable zonal isolation.
Background Major variables known to affect the quality of well cementing are as follows. (a) Casing design: Namely, type of casings used for surface, intermediate, and production zones, internal casing diameter, casing weight/length, centralization, and the relationship between casing and hole diameters. For horizontal wells, casing weight and centralization are very important as they affect the minimum standoff (measure of casing eccentricity) required to be above 60 % for uniform cement flow in the upper and lower parts of the horizontal segment of the annulus. The size of the annulus should lie within a given range (usually 0.75-1.5 inch]) [2]. (b) Cement properties and cementing procedure: Cement properties affect the overall bond quality more than any other variable. For both lead and tail cementing, important factors are slurry type, slurry density, rheological properties, free water content, fluid loss rate, thickening time, compressive strength, cement young modulus (Ycement should be less than Yrock [3]) and cement pumping rate. Spacer type and its volume also affect the cementing as it prevents intermixing of drilling fluid and cement.
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(c) Cement Additives: The type of additives used and their relative amount depend on the desired cement slurry properties. Additives include accelerator, retarder, extender, fluid loss, anti-foam, gas migration, dispersant, defoamer, antigel, expansion, and probably others, in required proportions. Each type of additive affects the cementing quality in its own way. (d) Rheological Properties of drilling fluid: Gel strength, plastic viscosity and yield stress affect the pumpability of the cement in the casing pipe and annulus. They also affect the displacement of drilling fluid by cement during cementing. (e) Other Conditions: Bottom hole circulation temperature (BHCT), bottom hole static temperature (BHST) and pressure inside the well are some of the conditions which affect the solidification of the cement slurry inside the annulus.
Theory Classification and regression are two modeling approaches used to build models that capture the effect of input variables on cementing quality. The output variable of the regression model is the BI value, whereas the classification model output variable is whether a well belongs to the leaking category or not. Dimensionless input variables are used for both models. Partial least squares (PLS) is used for the regression model [4] and partial least squares discriminant analysis (PLS-DA) is used for the classification model [5]. The VIP (Variable Importance in Projection) variable selection method and Leave-one-Out cross-validation methods were used with both PLS and PLS-DA models.
Results and Discussion Results of regression and classification modeling are summarized as below: PLS Regression Using CBL data, BI values were calculated at various depths for each of 23 wells, and subsequently the average and standard deviation of BI were computed for each well. The BI average and standard deviation were used as outputs of two PLS regression models (Model-1 and Model-2). Figure 1 shows cross-validation results and Figure 2 shows latent weights of selected variables (based on the VIP variable selection method) for the BI average. Model-2 did not produce satisfactory results, and consequently was not pursued further.
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Cross Validated (Bond Index) data
1.80 1.60 1.40 1.20
y = 1.0613x R² = 0.1661
1.00 0.80 0.60 0.40 0.20 0.00 0
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Experimental ( Bond Index) Data Figure 1: Cross-validation vs. Experimental BI data
Other Cement Additive Cement Additive(Gas migration) Cement Additive (Anti foam) Cement sacks/Annulus volume 1/Re Friction factor -0.40
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Figure 2: Latent variable weights for various dimensionless inputs
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PLS-DA Classification Analysis PLS-DA was applied on intermediate and production casing data. Based on SCP values, two categories were created. The first category represents wells with detectable gas leakage whereas the second category consists of wells with no gas leakage. Separate models were built for intermediate and production casing. Table 1 shows model fit and model validation results for both the casings. Table 1: Model fit and validation results for both casings Casing
Intermediate
Model Fit (Match success %)
Model Validation (Match success %) Category1:Leak Category2:No Category1:Leak Category2:No Leak Leak 84 85 79 81 Total : 84.5
Production
61
Total : 80 88
Total : 76
56
88 Total :74
It was found that not all input variables affect the outputs in similar manner. Rather, only few inputs are important for classification purposes. The importance of dimensionless input variables was studied in terms of corresponding VIP values. Figure 3 and Figure 4 show VIP values for intermediate and production casings, respectively. PLS regression and PLS-DA classification comparison On comparing cross-validation results obtained from regression analysis and classification analysis, it is evident that PLS-DA model has better predictive ability. In crossvalidation tests, PLS-DA models were able to correctly classify 80% and 74 % of the wells for intermediate and production casings, respectively; by contrast, PLS regression had poor predictive ability with low R2 = 0.16.
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pH Sulfates Extender Casing Weight/casing… Sacks/Annulus volume Depth/ Casing Internal Diameter Water to mix (bbls)/ Annulus volume Displacement rate/Cement Pump… Spacer volume (bbls)/Annulus… BHST/(Water Temperature) BHCT/(Water Temperature) Gel Strength( 10 min)/(Mud… 0.00
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VIP Figure 3: VIP (Variable Importance Projection) values of dimensionless inputs (Intermediate Casing) pH Anti gel(Lead) Anti foam(Tail) Anti foam(Lead) Spacer Volume/Annulus Vol. BHST(Lead)/ Water Temperature BHST(Tail)/ Water Temperature BHCT(Lead)/Water Temperature Plastic viscosity( Tail)* Casing… Yield Stress(Lead)*Casing Internal… 0
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VIP Figure 4: VIP (Variable Importance Projection) values of dimensionless inputs (Production Casing)
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Conclusion Data-driven classification models were developed that can predict the effect of several inputs variables on annular gas leakage for horizontal shale gas wells. Input variables can be adjusted to achieve better zonal isolation, resulting in lower remedial cementing cost and reduced undesired gas leakage. While the models developed are already reasonable at making useful predictions, future availability of more data points is expected to make model predictions more accurate and contribute towards greatly reducing the environmental risks from shale gas production. References 1. Fitzgerald, D.D., B.F. McGhee, and J.A. McGuire, GUIDELINES FOR 90% ACCURACY IN ZONE-ISOLATION DECISIONS. Journal of Petroleum Technology, 1985. 37(12): p. 2013-2022 2. Wilson, M.A. and F.L. Sabins, A laboratory investigation of cementing horizontal wells. SPE DRILL. ENGNG., 1988. 3(3 , Sep. 1988, p.275-280.). 3. Thiercelin, M.J., et al., Cement design based on cement mechanical response. SPE Drilling and Completion, 1998. 13(4): p. 266-273. 4. Dejong, S., Simpls - An Alternative Approach to Partial Least-Squares Regression. Chemometrics and Intelligent Laboratory Systems, 1993. 18(3): p. 251-263. 5. Barker, M. and W. Rayens, Partial least squares for classification. Journal of Chemometrics, 2003. 17(3): p. 166-173.
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