In horizontal-well, plug-and-perforate completions, various studies have shown that not all perforation clusters are stimulated equally. To increase perforation cluster treatment efficiency, engineers attempt to move the perforations of each stage to similarly-stressed rock. Most of these efforts have not included predictions quantifying efficiency improvements. This paper outlines a methodology for predicting improvements of perforation cluster treatment efficiency and includes a case study verifying the results of the model using pre-treatment diagnostics. In four Western Anadarko Basin wells, the operator measured mechanical rock properties using drill bit geomechanics. These properties were used to calculate the changes in minimum horizontal stress along each ~5,000-ft horizontal well. Within each treatment stage, the engineers chose perforation locations to minimize the difference in minimum horizontal stress. Using offset vertical logs and the geosteering interpretations, the engineers built a high-resolution fracture simulation model for each well. The model included the measured mechanical properties along the wellbore path. Comparing results from a geometric perforation model and the stress-balanced perforation model, the engineers predicted increased perforation cluster efficiencies between 10 and 20%. The four wells were completed using the stress-balanced perforation designs. Like all previous wells in the area, the operator performed step-down rate tests on these wells before each stimulation treatment. The step-down rate test is a common hydraulic fracturing diagnostic to quantify the number of open perforations taking treatment fluid. Compared to the operator's previous geometrically-perforated wells, the wells with the stress-balanced perforation designs showed more open perforations. A higher number of open perforations suggests a greater perforation cluster treatment efficiency. The increase in efficiency measured by the step-down rate tests was consistent with the model predictions. By understanding how stress-balancing perforation clusters will affect perforation cluster treatment efficiency, operators can optimize stimulations. The industry has not widely adopted stress-balanced perforation designs or other ‘engineered’ completion strategies. The results of ‘engineered’ completion studies have often been inconclusive, likely due to small sample sizes and reliance on production results. By combining affordable measurement of rock properties, modeled perforation cluster efficiency, and an affordable measurement of perforation efficiency, this paper provides a methodology for economically optimizing multi-stage stimulations in horizontal wells.

No abstract

No abstract

In this paper, the authors examine the impacts of natural fractures on the distribution of slurry in a well with a permanent fiber installation and drill bit geomechanics data. Additionally, they propose a framework for further investigation of natural fractures on slurry distribution. As part of the Marcellus Shale Energy and Environmental Laboratory (MSEEL), the operator monitored the drilling of a horizontal Marcellus Formation well with drill bit geomechanics, and subsequent stimulation phase with a DAS/DTS permanent fiber installation. Prior to the completion, the authors used an analytical model to examine the theoretical distribution of slurry between perforation clusters from a geomechanics framework. A perforation placement scheme was then developed to minimize the stress difference between clusters and to segment stages by the intensity of natural fractures while conforming to standard operating procedures for the operator's other completions. The operator initially began completing the well with the geomechanics-informed perforation placement plan while monitoring the treatment distribution with DAS/DTS in real time. The operator observed several anomalous stages with treating pressures high enough to cause operational concerns. The operator, fiber provider, and drill bit geomechanics provider reviewed the anomalous stages’ treatment data, DAS/DTS data, and geomechanics data and developed a working hypothesis. They believed that perforation clusters placed in naturally fractured rock were preferentially taking the treatment slurry. This phenomenon appeared to cause other clusters within the stage to sand-off or become dormant prematurely, resulting in elevated friction pressure. This working hypothesis was used to predict upcoming stages within the well that would be difficult to treat. Another perforation placement plan was developed for the second half of the well to avoid perforating natural fractures as an attempt to mitigate operational issues due to natural fracture dominated distribution. Over the past several years, the industry's growing understanding of geomechanical and well construction variability has created new limited-entry design considerations to optimize completion economics and reduce the variability in cluster slurry volumes. Completion engineers working in naturally fractured fields, such as the Marcellus, should consider the impact the natural fractures have on slurry distribution when optimizing their limited-entry designs and stage plan.

Unexpected problems during completion create costs that can cause a well to be outside its planned AFE and even uneconomic. These problems range from merely experiencing abnormally high pressures during treatment to casing failures. The authors of this paper use machine learning methods combined with geomechanical, wellbore trajectory, and completion datasets to develop models that predict which stages will experience difficulties during completion. The operator collected geomechanical data for 26 lateral wells. Several of these wells experienced significant difficulties during completion, including casing failures. By examining the completion data for the study wells, engineers developed two objective proxies for trouble stages: a high fracture gradient cut-off and a sand-to-water ratio cut off. The geomechanical data were combined with wellbore trajectory and stage-level completion data. Machine learning techniques were then used to predict the fracture gradient at the end of treatment and the sand-to-water ratio of a stage. The data were subdivided by target horizon to ensure the model results were not skewed towards a specific horizon. The models were cross validated, hyper-parameter tuned, and then tested against a subset of the data withheld from the training algorithm (the test data subset). Multiple metrics were applied to measure the effectiveness of the models. For the fracture gradient regression, the model that minimized the root mean squared error (RMSE) was chosen. This model was compared to a prediction that each stage would have a fracture gradient equal to the average fracture gradient. The correlation between predicted and measured frac gradients was also calculated. These metrics were analyzed for both the entire data set and the test data subset. By all these measures the model performs significantly better than the control prediction. For the sand-to-water ratio metric, the model that maximized the receiver operating characteristic area under the curve (ROC-AUC or AUC) score was chosen. We chose to measure the success of the model with the precision of its prediction of outlier (trouble) stages on the test data subset. The model performs with a precision of at least 73%. Both models identify stages where casing failures occurred as trouble stages. With these initial results, the authors are confident they can predict the probability of any stage causing a significant operational disruption in these horizons. By predicting the probability of a trouble stage, mitigation plans can be implemented to reduce costs ahead of time. By further analyzing the models, engineers can identify the major drivers for the trouble stages and work to reduce them as early as the well planning stage.