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Summary Sucker rod pumps provide a cost‐efficient way to produce hydrocarbons from low‐pressure reservoir formations. Their design is dependent on predictive models used to optimize the system before implementation in the field. The greater the accuracy of these models, the better the performance of the pumping system in the field. The scope of this paper is to present an improved plunger slippage model, developed in connection to the pump test facility (PTF) and validated by field data. This paper provides an analysis of plunger slippage. Existing plunger‐slippage models are compared with field data. Based on the result of this comparison, an improved plunger‐slippage model is derived based on the Navier‐Stokes equation and dimensional analysis. Adjustments are applied to increase the model's validity. The mathematical and laboratory work have shown that a proper fit to reality requires four coefficients that make the equation an empirical one. An extensive laboratory test campaign, using real field equipment, was performed at the PTF at Montanuniversitaet Leoben (MUL). Numerous influencing parameters, such as plunger velocity, clearance magnitude, and fluid viscosity, were studied. Historical plunger‐slippage models overestimate the slippage rate, whereas field data showed that newer models underestimate the slippage rate. In general, dynamic models are more accurate than static slippage models. The fit of the four model coefficients, based on laboratory tests, indicate that the chosen strategy of using laboratory tests and allocating the results to field conditions has worked out. The comparison of the results obtained by the presented improved slippage model and the field tests indicate a good match. The presented slippage model predicts the plunger slippage rate precisely and results in greater accuracy. The plunger wear rate approach is presented, which can be used to plan well interventions, decrease intervention costs, and increase the mean time between failures.
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.
Dontsov, Egor (ResFrac Corporation) | Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Quinn, Christopher (W. D. Von Gonten Laboratories) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Hines, Chris (BP America Production Company, BPx Energy Inc.) | Montgomery, Ryan (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract As the number of wells drilled in regions with existing producing wells increases, understanding the detrimental impact of these by the depleted zone around parent wells becomes more urgent and important. This understanding should include being able to predict the extent and heterogeneity of the depleted region near the pre-existing wells, the resulting altered stress field, and the effect of this on newly created fractures from adjacent child wells. In this paper we present a workflow that addresses the above concern in the Eagle Ford shale play, using numerical simulations of fracturing and reservoir flow, to define the effect of the depletion zone on child wells and match their field production data. We utilize an ultra-fast hydraulic fracture and depletion model to conduct several hundred numerical simulations, with varying values of permeability and surface area, seeking for cases that match the field production data. Multiple solutions exist that match the field data equally well, and we used additional field production data of parent-child well-interaction, to select the most plausible model. Results show that the depletion zone is strongly non-uniform and that large reservoir regions remain undepleted. We observe two important effects of the depleted zone on fractures from child wells drilled adjacent to the parents. Some fractures propagate towards low pressure zones and do not contribute to production. Others are repelled by the higher stress region that develops around the depletion zone, propagate into undepleted rock, and have production rates commensurate to that from other child wells drilled away from depleted region. The observations are validated by the field data. Results are being used to optimize well placement and well spacing for subsequent field operations, with the objective to increase the effectiveness of the child wells.
A new Chevron-led work flow is allowing the oil company to marry both organic field data with physics-based simulation models and machine-learning techniques to arrive at a more accurate prediction of well performance and, ultimately, a reliable production forecast for unconventional oil fields. Standard production forecast techniques for unconventional asset development rely mostly on field data, which can suffer from limitations in both data quality and quantity. Interpreting subsurface dynamics directly from field observations is also a challenge. Popular methods such as decline curve analysis can be hampered by limited data samples and too many variables. Reservoir simulation depends mostly on finding a good history match for the current field, but this method is resource-intensive and requires certain expertise.
Abstract This study presents a hybrid approach that combines data-driven and physics models for worn and sharp drilling simulation of polycrystalline diamond compact (PDC) bit designs and field learning from limited downhole drilling data, worn state measurements, formation properties, and operating environment. The physics models include a drilling response model for cutting forces, worn or rubbing elements in the bit design. Decades of pressurized drilling and cutting experiments validated these models and constrained the physical behaviour while some coefficients are open for field model learning. This hybrid approach of drilling physics with data learning extends the laboratory results to application in the field. The field learning process included selecting runs in a well for which rock properties model was built. Downhole drilling measurements, known sharp bit design, and measured wear geometry were used for verification. The models derived from this collaborative study resulted in improved worn bit drilling response understanding, and quantitative prediction models, which are foundational frameworks for drilling and economics optimization.
Summary In this paper, we present an accurate semiempirical rate of penetration (ROP) predictive model for polycrystalline diamond compact (PDC) bits. Our model is inspired by the model of Bourgoyne and Young (B&Y) and follows an exponential form with 10 different drilling functions to account for various factors affecting ROP in drilling operations. We extend the B&Y model to the PDC bits and discuss that a different predictive model should be obtained for each formation. On top of the factors included in the original B&Y model, our model accounts for parameters such as downhole motor, equivalent circulating density, mechanical weight on bit (WOB), and wellbore inclination. In particular, we incorporate the effect of equilibrium cuttings bed thickness and downhole cuttings concentration in the ROP model. The parameters of the model are obtained using multiple regression analysis with the field data. The importance of obtaining a formation‐based ROP model is tested and verified with field data, and an algorithm to determine the parameters for new data is provided. The model can be incorporated in a framework to obtain an optimal well plan for a new well or for prescribing optimal operational parameters for well planning and real‐time drilling operations. The prediction performance of the proposed model is also evaluated in various formations for several test wells across an offshore gas field. Our results indicate that the proposed model is able to predict the drilling ROP with an accuracy of more than 90%.
Fracture diagnostic techniques are divided into several groups. Direct far-field methods consists of tiltmeter-fracture-mapping and microseismic-fracture-mapping techniques. These techniques require sophisticated instrumentation embedded in boreholes surrounding the well to be fracture treated. When a hydraulic fracture is created, the expansion of the fracture causes the earth around the fracture to deform. Tiltmeters can be used to measure the deformation and to compute the approximate direction and size of the created fracture.
The single well chemical tracer (SWCT) test can be used to evaluate an Improved oil recovery (IOR) process quickly and inexpensively. The one-spot procedure takes advantage of the nondestructive nature of the SWCT method. The single-well (one-spot) pilot is carried out in three steps. First, Sor for the target interval is measured (see Residual oil evaluation using single well chemical tracer test. Then an appropriate volume of the IOR fluid is injected into the test interval and pushed away from the well with water.
The recent proliferation of subsurface data from instrumented wells has created significant challenges for traditional production-data-analysis methods to extract useful information for reservoir management. The approach has the potential to be used as a big-data analytic tool for long-duration production-data analysis to serve as a screening tool for selection of restimulation candidates. Restimulation treatments in producing shale wells have the potential to improve economic performance by increasing the conductivity of existing fractures or enhancing their contact with the formation. The influence of matrix and fracture characteristics on the success of restimulation, however, is not completely understood, which has led to uncertainty in determining favorable candidate wells. Several methods to select restimulation candidates have been proposed.
Kirkuk is a supergiant oil reservoir located in Iraq. Kirkuk began production in 1934, and 2 billion bbl of oil were produced before water injection was implemented in 1961. From 1961 to 1971, 3.2 billion bbl of oil were produced under pressure maintenance by waterdrive using river water. The 1971 production rate was approximately 1.1 million barrels of oil per day (BOPD). Since then, the field has continued to produce large volumes of oil by voidage-replacement water injection; however, few production details for recent years appear in the technical literature.