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Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Sovizi, Javad (Baker Hughes) | Dontsov, Egor (ResFrac Corporation) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Blose, Matthew (BP America Production Company, BPx Energy Inc.) | Hailu, Thomas (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.
Abstract Reservoir A is being developed in early and interim phases in order to gather static & dynamic data to minimize the risk associated to subsurface uncertainties. In early and interim phases, only production is taking places. During full field, water injection scheme will be implemented using mainly 5-spot pattern. It is very crucial to measure the subsurface uncertainties and their impact on the reservoir development. For this purpose, the uncertainty parameters are identified and their ranges are selected based on the current well performances during probabilistic History matching (PHM) phase. In full field runs, the uncertain subsurface parameters are quantified to prioritize the future reservoir monitoring and data gathering plans. Note that wells are equipped with the permanent downhole pressure gauges. Reservoir A is one of the major reservoirs of a green-field located offshore Abu Dhabi and is being developed with a 5-spot water injection pattern. The producers and water injectors are horizontal wells which are drilled across different flow unit within the reservoir. The reservoir properties are variable across all the flow units, which may results in a non-uniform water front. Being a green field, there are more uncertainties as compared to the brown field. More than three years production & pressure data is available which is used in this uncertainty study. This production data is mainly used to achieve the probabilistic History match on well-wise basis. In this uncertainty study, previous HM parameters are removed. However, based on previous history matching learnings, the subsurface uncertain parameters ranges are selected for this probabilistic History match phase. The criteria for filtering the valid runs during this phase are set to be ±150 Psi compared to the actual downhole pressure readings. In case of decreasing this filtering range to 75 Psi, results in reduction in the reserve range in P90 to P10. Based on ±150 Psi principle, the subsurface parameter ranges are furthered reformed for full field uncertainty study/run. The industry standard workflow is followed to quantify the subsurface parameters during this phase. In this study, we used the Permeability modifiers based on RRT, Faults transmisibilities, Relative Perm curves (based on SCAL data), Kv/Kh ratio (from PTA), etc. as uncertain parameters. The impact of each parameter is measured and quantified with respect to plateau and total reserves.
Rig automation projects can benefit from the experiences developed during the successful automation of rig pipe-handling equipment. The data provided were derived from the operation and development of iron roughnecks and pipe-handling systems in the field. This paper includes a study of failure mechanisms and how they affect reliability, presents maintenance experiences to show the impact of automation on crew capability and training, and describes many operational and design pitfalls.
Four years ago, Varco Intl. Inc. installed fully integrated, automated pipe-handling machines (PHM's) on two jackup rigs and one semisubmersible rig. The PHM unit on the semisubmersible was removed in 1989 primarily because the operator was not satisfied with its reliability. The PHM's on the jackups operate in the North Sea and have undergone extensive modifications as a result of the information learned during their operation. These modifications include major changes to the control hardware, hydraulic systems, and many of the control components. Many of the changes resulted from activities and operations not anticipated during the original design of the system. Today, these two PHM's are considered successful state-of-the-art automated pipe-handling systems, and seven additional units have been ordered. One of these new units is operational and two more have been installed. The PHM's trip drillpipe and drill collars up to 9% in. [248 mm] in diameter without floor hands or a derrickman.
The PHM trips drillpipe and drill collar stands in and out of the well by remote control. This machine has spinning, torquing, lifting, and rack stand positioning. Each stand has fingerboard locking. Many operations, such as stand makeup and breakout, are performed automatically.
The PHM (Fig. 1) consists of a column assembly that runs from the drill floor to the racking board, upon which two extending arms and an iron roughneck assembly are mounted. The columns move between well center and setback and can rotate 90° to the left or right. The arms are pivot-mounted and mechanically interconnected to operate in parallel. Hydraulic clamping jaws at the ends of the arms hold drillpipe and drill collars from 3½ to 9 ¾ in. [88.9 to 248 mm] in diameter.