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Summary Ultra‐high‐pressure high‐temperature (uHPHT) reservoirs undergo extreme pressure depletion during their production life cycle. This results in significant reservoir compaction and consequent overburden subsidence with major consequences for wellbore mechanical integrity, safety, and field economics. However, the use of underdetermined geomechanical models to accurately predict compaction‐induced stress/strain changes on wellbores and its consequences during production time results in significant residual uncertainty. One method of measuring compaction‐induced stress/strain changes in wellbore is by the emplacement and measurement of radioactive markers. Although it is long established in normal pressure reservoirs, it is rare in uHPHT projects. The Culzean uHPHT gas‐condensate field is located in the UK Central North Sea. To constrain geomechanical model compaction uncertainty, radioactive markers were deployed. The objective was to accurately acquire preproduction baseline measurements and subsequent changes through periodic measurements during production life. These accurate wellbore measurements would then be compared with the geomechanical model to help calibrate predicted to actual compaction. By doing so, the objective is to enable better informed decisions regarding well and field management. The Culzean uHPHT radioactive marker project comprised a planning phase and a preproduction safe deployment including a baseline survey phase. Subsequent repeat measurements are planned during field production life. The emplacement and surveying of the subsurface radioactive markers for compaction monitoring in uHPHT reservoirs is a high value but nontrivial operation. In addition, much knowledge and experience of the methodology has been lost. This paper contributes to published literature by describing the successful emplacement and monitoring of subsurface radioactive markers on Culzean and aims to capture learnings and knowledge for future workers. Early detailed planning coupled with extensive testing is key to successful deployment. Timely engagement of all stakeholders and ensuring all decisions are aligned with safety and environmental considerations also contribute to realization of the project aims.
Pan, Jin (Wuhan University of Technology, Wuhan) | Wang, Tao (Wuhan University of Technology, Wuhan) | Xu, Ming Cai (Huazhong University of Science and Technology, Wuhan / Collaborative Innovation Centre for Advanced Ship and Deep-Sea Exploration) | Gao, Gui (Wuhan-Jiujiang Railway Passenger Transportation Hubei Co. Ltd.)
The hull block erection network process, which is performed during the master production planning stage of the shipyard, is frequently delayed because of limited resources, workspace, and block preparation ratio. In this study, a study to predict the delay with respect to the block erection schedule is conducted by considering the variability of the block preparation ratio based on the discrete event simulation algorithm. It is confirmed that the variation of the key event observance ratio is confirmed according to the variability caused by the block erection process, which has the minimum lead time in a limited resource environment, and the block preparation ratio. Furthermore, the optimal pitch value for the key event concordance is calculated based on simulation results.
The conventional wisdom about artificial intelligence is that bigger is better. Consider neural networks, the software used for training algorithms that can discover patterns within information that humans may miss. Researchers are creating increasingly huge versions that can analyze massive amounts of data, and thus do things such as generate more realistic text in response to a query. But these enormous neural networks come with some costs, including making it difficult for researchers to figure out how and why the software makes its predictions and decisions. When these neural networks become so big, researchers can get lost attempting to make sense of the billions of interconnected calculations taking place.
Abstract In this case study, we apply a novel fracture imaging and interpretation workflow to take a systematic look at hydraulic fractures captured during thorugh fracture coring at the Hydraulic Fracturing Test Site (HFTS) in Midland Basin. Digital fracture maps rendered using high resolution 3D laser scans are analyzed for fracture morphology and roughness. Analysis of hydraulic fracture faces show that the roughness varies systematically in clusters with average cluster separation of approximately 20' along the core. While isolated smooth hydraulic fractures are observed in the dataset, very rough fractures are found to be accompanied by proximal smoother fractures. Roughness distribution also helps understand the effect of stresses on fracture distribution. Locally, fracture roughness seems to vary with fracture orientations indicating possible inter-fracture stress effects. At the scale of stage lengths however, we see evidence of inter-stage stress effects. We also observe fracture morphology being strongly driven by rock properties and changes in lithology. Identified proppant distribution along the cored interval is also correlated with roughness variations and we observe strong positive correlation between proppant concentrations and fracture roughness at the local scale. Finally, based on the observed distribution of hydraulic fracture properties, we propose a conceptual spatio-temporal model of fracture propagation which can help explain the hydraulic fracture roughness distribution and ties in other observations as well.
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 The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development
Abstract A breakthrough patent-pending pressure diagnostic technique using offset sealed wellbores as monitoring sources was introduced at the 2020 Hydraulic Fracturing Technology Conference. This technique quantifies various hydraulic fracture parameters using only a surface gauge mounted on the sealed wellbore(s). The initial concept, operational processes, and analysis techniques were developed and deployed by Devon Energy. By scaling and automating the process, Sealed Wellbore Pressure Monitoring (SWPM) is now available to the industry as a repeatable workflow that greatly reduces analysis time and improves visualizations to aid data interpretations. The authors successfully automated the SWPM analysis procedure using a cloud-based software platform designed to ingest, process, and analyze high-frequency hydraulic fracturing data. The minimum data for the analysis consists of the standard frac treatment data combined with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore(s) while actively fracturing offset wells. The result is a rapid, repeatable SWPM analysis that minimizes individual interpretation biases. The primary deliverables from SWPM analyses are the Volumes to First Response (VFR) on a per stage basis. In many projects, multiple pressure responses within a single stage have been observed, which provides valuable insight into fracture network complexity and cluster/stage efficiency. Various methods are used to visualize and statistically analyze the data. A scalable process facilitates creating a statistical database for comparing completion designs that can be segmented by play, formation, or other geological variations. Completion designs can then be optimized based upon the observed well responses. With enough observations and based on certain spacings, probabilities of when to expect fracture interactions could be assigned for different plays.
Abstract The subject of this paper is the application of a unique machine learning approach to the evaluation of Wolfcamp B completions. A database consisting of Reservoir, Completion, Frac and Production information from 301 Multi-Fractured Horizontal Wolfcamp B Completions was assembled. These completions were from a 10-County area located in the Texas portion of the Permian Basin. Within this database there is a wide variation in completion design from many operators; lateral lengths ranging from a low of about 4,000 ft to a high of almost 15,000 ft, proppant intensities from 500 to 4,000 lb/ft and frac stage spacing from 59 to 769 ft. Two independent self-organizing data mappings (SOM) were performed; the first on completion and frac stage parameters, the second on reservoir and geology. Characteristics for wells assigned to each SOM bin were determined. These two mappings were then combined into a reservoir type vs completion type matrix. This type of approach is intended to remove systemactic errors in measuement, bias and inconsistencies in the database so that more realistic assessments about well performance can be made. Production for completion and reservoir type combinations were determined. As a final step, a feed forward neural network (ANN) model was developed from the mapped data. This model was used to estimate Wolfcamp B production and economics for completion and frac designs. In the performance of this project, it became apparent that the incorporation of reservoir data was essential to understanding the impact of completion and frac design on multi-fractured horizontal Wolfcamp B well production and economic performance. As we would expect, wells with the most permeability, higher pore pressure, effective porosity and lower water saturation have the greatest potential for hydrocarbon production. The most effective completion types have an optimum combination of proppant intensity, fluid intensity, treatment rate, frac stage spacing and perforation clustering. This paper will be of interest to anyone optimizing hydraulically fractured Wolfcamp B completion design or evaluating Permian Basin prospects. Also, of interest is the impact of reservoir and completion characteristics such as permeability, porosity, water saturation, pressure, offset well production, proppant intensity, fluid intensity, frac stage spacing and lateral length on well production and economics. The methodology used to evaluate the impact of reservoir and completion parameters for this Wolfcamp project is unique and novel. In addition, compared to other methodologies, it is low cost and fast. And though the focus of this paper is on the Wolfcamp B Formation in the Midland Basin, this approach and workflow can be applied to any formation in any Basin, provided sufficient data is available.
Mondal, Somnath (Shell Exploration & Production Co.) | Zhang, Min (University of Texas at Austin) | Huckabee, Paul (Shell Exploration & Production Co.) | Ugueto, Gustavo (Shell Exploration & Production Co.) | Jones, Raymond (Shell Exploration & Production Co.) | Vitthal, Sanjay (Shell Exploration & Production Co.) | Nasse, David (Shell Exploration & Production Co.) | Sharma, Mukul (University of Texas at Austin)
Abstract This paper presents advancements in step-down-test (SDT) interpretation to better design perforation clusters. The methods provided here allow us to better estimate the pressure drop in perforations and near-wellbore tortuosity in hydraulic fracturing treatments. Data is presented from field tests from fracturing stages with different completion architectures across multiple basins including Permian Delaware, Vaca Muerta, Montney, and Utica. The sensitivity of near-wellbore pressure drops and perforation size on stimulation distribution effectiveness in plug-and-perf (PnP) treatments is modeled using a coupled hydraulic fracturing simulator. This advanced analysis of SDT data enables us to improve stimulation distribution effectiveness in multi-cluster or multiple entry completions. This analysis goes much further than the methodology presented in URTeC2019-1141 and additional examples are presented to illustrate its advantages. In a typical SDT, the injection flowrate is reduced in four or five abrupt decrements or "steps", each with a duration long enough for the rate and pressure to stabilize. The pressure-rate response is used to estimate the magnitude of perforation efficiency and near-wellbore tortuosity. In this paper, two SDTs with clean fluids were conducted in each stage - one before and another after proppant slurry was injected. SDTs were conducted in cemented single-point entry (cSPE) sleeves, which present a unique opportunity to measure only near-wellbore tortuosity using bottom-hole pressure gauge at sleeve depth, negligible perforation pressure drops, and less uncertainty in interpretation. SDTs were conducted in PnP stages in multiple unconventional basins. The results from one set of PnP stages with optic fiber distributed sensing were modeled with a hydraulic fracturing simulator that combines wellbore proppant transport, perforation size growth, near-wellbore pressure drop, and hydraulic fracture propagation. Past SDT analysis assumed that the pressure drop due to near-wellbore tortuosity is proportional to the flow rate raised to an exponent, β = 0.5, which typically overestimates perforation friction from SDTs. Theoretical derivations show that β is related to the geometry and flow type in the near-wellbore region. Results show that initial β (before proppant slurry) is typically around 0.5, but the final value of β (after proppant slurry) is approximately 1, likely due to the erosion of near-wellbore tortuosity by the proppant slurry. The new methodology incorporates the increase in β due proppant slurry erosion. Hydraulic fracturing modeling, calibrated with optic fiber data, demonstrates that the stimulation distribution effectiveness must consider the interdependence of proppant segregation in the wellbore, perforation erosion, and near-wellbore tortuosity. An improved methodology is presented to quantify the magnitude of perforation and near-wellbore tortuosity related pressure drops before and after pumping of proppant slurry in typical PnP hydraulic fracture stimulations. The workflow presented here shows how the uncertainties in the magnitude of near-wellbore complexity and perforation size, along with uncertainties in hydraulic fracture propagation parameters, can be incorporated in perforation cluster design.
Abstract Assessment of in-situ stresses and hydraulic fracturing stimulation are two critical parameters for successful heat extraction from Enhanced Geothermal Systems (EGS). Fracture injection and injection/flow back tests are two conventional techniques for estimating the minimum horizontal stress in subsurface formations. Because of the heat exchange during the test, ultra-low permeability of the host rock, and natural fractures, the conventional methods yield inaccurate results in geothermal reservoirs. In this paper, we present a new methodology based on the signal processing approach for analyzing DFIT in geothermal reservoirs. The applicability of our technique is demonstrated using several test data from the Utah FORGE project. The main advantage of our methodology is that it does not depend on any assumption regarding fracture geometry and rock properties. Also, unlike most similar studies, we consider the effect of heat exchange between fracturing fluid and the hot rock. In our methodology, the recorded pressure and temperature are treated as signals, and a wavelet transform is applied to separate them to high pass (noise) and low pass (approximation) components. Using the noise energy of the two signals, we then identify different events such as fracture closure. Also, an analytical technique is used to correct the pressure by extracting the effect of fluid compressibility and heat exchange between the rock and injected fluid. We show that the G-Function technique underestimates the minimum horizontal stress in tight formations. After applying the corrections for pressure, the underestimation becomes more apparent. However, our approach gives consistent results before and after the pressure correction. Using the developed technique, we analyzed several injection tests from the Utah FORGE project. Both recorded pressure and temperature have been analyzed. Results show that the energy of the pressure signal noise decreases to a minimum level at the fracture closure. The fracture closure is confirmed by applying the same technique on the recorded temperature. The moment of closure using the proposed methodology is compared to the G-function approach, before and after correction of the pressure for temperature. Unlike physics-based techniques, the proposed method does not have any pre-assumption about the fracture's geometry or type of the well. The method solely relies on the pressure and temperature signals that are recorded during the injection and shut-in periods. Combining several analysis techniques to analyze DFIT (including the analysis of monitored temperature for a geothermal reservoir) is unique and maybe the first of its kind.