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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
Kebert, Brent (Colorado School of Mines) | Almulhim, Abdulraof (Colorado School of Mines) | Miskimins, Jennifer (Colorado School of Mines) | Hunter, William (Ovintiv Inc) | Soehner, Gage (Ovintiv Inc)
Abstract Successfully treating each cluster within a hydraulic fracturing stage is a key objective for "plug-n-perf" well completions. Most operating companies would agree that the main underlying desire for a successful completion is related to future production capability. In unconventional reservoirs, propped and conductive hydraulic fractures are the primary completion result that drives production and reserve recovery. When designing a treatment, the spacing of clusters is critical to optimizing production and reserve recovery parameters, and therefore, even proppant distribution across a single stage delivers a well the greatest potential for optimized production performance. Diverting the fracturing fluid and proppant evenly across the clusters in a stage allows the greatest opportunity for each cluster to produce equally and drain the associated reservoir volume. Generating equal, producing fractures across a horizontal wellbore is a difficult problem that operators are still trying to solve. This work models the fluid and proppant distribution across a field-scale, 250-ft long, horizontal hydraulic fracturing stage, replicating realistic field conditions. By utilizing computational fluid dynamics (CFD), this paper investigates the effected proppant distribution results from a fracturing stage mimicking the presence of both a leaking plug and the impacts of stress shadowing. The proppant concentration throughout the wellbore, along with internal wellbore pressure and velocity, are also reviewed to gain an understanding of the effect of the field conditions. Additionally, this paper illustrates the effect of different proppant "ramping" conditions during the fracturing stage. Proppant ramping schedules can be smooth or sharp when increasing proppant concentration, which alters the proppant concentrations throughout the wellbore and associated perforation clusters. Unanticipated alterations of the proppant concentration within the wellbore can lead to early screenouts. Gaining a better understanding of the proppant distribution and concentration inside the wellbore can lead to improved designs of hydraulic fracturing completions.
Liu, Xinghui (Chevron Corporation) | Wang, Jiehao (Chevron Corporation) | Singh, Amit (Chevron Corporation) | Rijken, Margaretha (Chevron Corporation) | Chrusch, Larry (Chevron Corporation) | Wehunt, Dean (Chevron Corporation) | Ahmad, Faraj (Colorado School of Mines) | Miskimins, Jennifer (Colorado School of Mines)
Abstract Multi-stage plug-n-perf fracturing of horizontal wells has proven to be an effective method to develop unconventional reservoirs. Various studies have shown uneven fluid and proppant distributions across all perforation clusters. It is commonly believed that both fracturing fluid and proppant contribute to unconventional well performance. Achieving uniform fluid and proppant placement is an important step toward optimal stimulation. This paper discusses how to achieve such uniform placement in each stage via a CFD (Computational Fluid Dynamics) modeling approach. CFD models in several lab scales were built and calibrated using experimental data of proppant transport through horizontal pipes in several laboratory configurations. A field-scale model was then built and validated using perforation erosion data from downhole camera observations and the same model parameters calibrated in the lab-scale model. With the field-scale model validated, CFD simulations were performed to evaluate the impact of key parameters on fluid and proppant placement in individual perforations and clusters. Some key parameters investigated in this study included perforation parameters (size, orientation, number), cluster spacing, cluster count per stage, fluid properties, proppant properties, pumping rates, casing sizes, and stress shadow effects, etc. Both lab and CFD results show that bottom-side perforations receive significantly more proppant than top-side perforations due to gravitational effects. Lab and CFD results also show that proppant distribution is increasingly toe-biased at higher rates. Proppant concentration along the wellbore from heel to toe generally varies significantly. Gravity, momentum, viscous drag, and turbulent dispersion are key factors affecting proppant transport in horizontal wellbores. This study demonstrates that near-uniform fluid and proppant placement across all clusters in each stage is achievable by optimizing perforation, cluster, and other treatment design factors. Validated CFD modeling plays an important role in this design optimization process.
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.
Abstract Proppant transport in horizontal wellbores has received significant industry focus over the past decade. One of the most challenging tasks in the hydraulic fracturing of a horizontal well is to predict the proppant concentration that enters each perforation cluster within the same stage. The main objective of this research is to investigate the effect of different limited-entry perforation configurations on proppant transport, settling, and distribution across different perforation clusters in multistage horizontal wells. To simulate a fracturing stage in a horizontal wellbore, a laboratory-based 30-foot horizontal clear apparatus with three perforation clusters is used. Fresh water (~1 cp) is utilized as the carrier fluid to transport the proppant. This research incorporates the effect of testing three different injection rates each at four different proppant concentrations on proppant transport. Different limited-entry perforation configurations are also used to test the perforation effect on proppant transport using similar injection rates and proppant concentrations for the same proppant size. The proppant is mixed with fresh water in a 200-gallon tank for at least 10 minutes to ensure the consistency of the slurry mixture. The mixture is then injected into the transparent horizontal wellbore through a slurry pump. This laboratory apparatus also includes a variable frequency drive, a flow meter, and two pressure transducers located right before the first two perforation clusters. Sieve analysis is conducted to understand the ability of fresh water to carry bigger particles of the mixture at different injection rates, proppant concentrations, and perforation configurations. The results show different fluid and proppant distributions occur when altering the perforation configurations, injection rates, and proppant concentrations. The effect of gravity is extreme when using a limited entry configuration at each cluster (1 SPF) located at the bottom of the pipe, especially at low injection rates, resulting in uneven proppant distribution with a heal-biased distribution. However, even proppant distribution is observed by changing the limited entry perforation configuration to the top of the horizontal pipe at similar injection rates and low proppant concentration. Increasing the proppant concentration reduces the void spaces between the particles and pushes them away toward the toe cluster. Even proppant distribution is also observed across the three perforation clusters when using high flow rates and a 2 SPF perforation configuration located at both the top and the bottom of the pipe. The results of the sieve analyses show different size distributions of the settled and exited proppant through different perforations and clusters. This illustrates the ability of fresh water to transport different percentages of different proppant sizes to different perforations and clusters within a single stage. Frequently, the injected proppant is assumed to be distributed evenly across the perforation clusters and that the distribution of fluid and proppant is identical. However, this research adds data to the portfolio that this assumption is generally not valid. Additionally, the distribution of the transported proppant is observed to be different across individual clusters and different perforations within each cluster. Such information is beneficial to understanding transport in horizontal, multi-stage completions and how such impacts the overall treatment efficiency, especially when employing limited-entry perforation techniques.
Abstract The main functions of hydraulic fracturing fluids are to create a fracture network and to carry and place the proppant into the created fractures networks, thus, adding to fracture conductivity. Significant research has been performed to develop ideal fracturing fluid systems. The development focus has mainly been on optimization of a fluid rheology that can transport and place the proppant into the primary and any subsidiary fractures with less damage to the formation and at a lower cost. The main goal of this work is to add to the understanding and optimization of proppant transport in complex hydraulic fracture networks. Specifically for this study, focus is placed on two different fluids, water-glycerin solution and water-sodium chloride solution, representing varying fluid densities and viscosities. The effects of changing fluid viscosities, densities, proppant densities, proppant sizes, proppant concentrations, and slurry injection rates on proppant transport were then experimentally investigated. This experimental work shows that viscosity has a greater impact on the proppant transport than fluid density does, thus implying a larger impact on the resulting fracture conductivity. The results of this work show that a water-glycerin solution, with a viscosity of 4.3 cp, has significant proppant carrying capacity with proppants delivered uniformly to greater distances. On the other hand, the results show that a water-sodium chloride solution of 9.24 ppg density has less capability to carry the proppant deep into the fractures indicating that viscosity has a greater impact on the proppant transport than fluid density does. The lab results also showed that increasing proppant concentrations and injection rates has a positive impact on proppant transport.
Abstract This paper explores a holistic approach to characterize trouble stages by applying automated event recognition of abnormal pressure increases and associating those events to formation and operational causes. This analysis of pressure increases provides insight into the potential causes of operational difficulties, and the related diagnostics can suggest improvements to future pump schedules. Improving how stages are pumped is profitable both in the short-term (reducing wasted fluid and chemicals, and other remediation measures) and in the long-term (increased well productivity). Quantifying how design decisions ultimately affect operations can help decrease the frequency of operational problems and help realize these gains. In this study, the identification of problematic frac stages was initially performed manually (stage-by-stage) using a cloud-based hydraulic fracture data application. During this process, the team recognized that the problem stages had their own characteristic pressure signature - a sudden unexplained pressure increase in the absence of rate changes. A machine learning algorithm was then developed to automatically identify this type of signature. Additionally, previously published machine learning algorithms were used to recognize other operational events of interest, e.g., when proppant reaches the perforations. Then by combining the various events and creating short search windows around each abnormal pressure increase, it is possible to find concurrent operations that may be associated with the observed pressure behavior. A subsequent statistical analysis revealed that abnormal pressure increases often coincided with changes in proppant concentration in problem stages (stages with abnormal treating pressure behavior). This behavior may be due to near-wellbore effects caused by the changing fluid flow dynamics. Furthermore, it was observed that treating pressures that behaved contrary to hydrostatic pressure effects may be useful in identifying when injectivity is lost and provide an early signal for screen outs. Through this holistic approach, we were able to identify trouble stages and discern some diagnostics for automated detection of abnormal treating pressure increases. The team was able to identify areas within the stages that were inefficiently pumped, resulting in cost-savings through optimization of proppant and friction reducer (FR) loadings while maintaining a level of caution to prevent screen outs. Finally, the automated detection of pressure anomalies offers a pathway to the real-time prediction and avoidance of operational difficulties such as pressure outs and screen outs.
Abstract Breakdown pressure is the peak pressure attained when fluid is injected into a borehole until fracturing occurs. Hydraulic fracturing operations are conducted above the breakdown pressure, at which the rock formation fractures and allows fluids to flow inside. This value is essential to obtain formation stress measurements. The objective of this study is to automate the selection of breakdown pressure flags on time series fracture data using a novel algorithm in lieu of an artificial neural network. This study is based on high-frequency treatment data collected from a cloud-based software. The comma separated (.csv) files include treating pressure (TP), slurry rate (SR), and bottomhole proppant concentration (BHPC) with defined start and end time flags. Using feature engineering, the model calculates the rate of change of treating pressure (dtp_1st) slurry rate (dsr_1st), and bottomhole proppant concentration (dbhpc_1st). An algorithm isolates the initial area of the treatment plot before proppant reaches the perforations, the slurry rate is constant, and the pressure increases. The first approach uses a neural network trained with 872 stages to isolate the breakdown pressure area. The expert rule-based approach finds the highest pressure spikes where SR is constant. Then, a refining function finds the maximum treating pressure value and returns its job time as the predicted breakdown pressure flag. Due to the complexity of unconventional reservoirs, the treatment plots may show pressure changes while the slurry rate is constant multiple times during the same stage. The diverse behavior of the breakdown pressure inhibits an artificial neural network's ability to find one "consistent pattern" across the stage. The multiple patterns found through the stage makes it difficult to select an area to find the breakdown pressure value. Testing this complex model worked moderately well, but it made the computational time too high for deployment. On the other hand, the automation algorithm uses rules to find the breakdown pressure value with its location within the stage. The breakdown flag model was validated with 102 stages and tested with 775 stages, returning the location and values corresponding to the highest pressure point. Results show that 86% of the predicted breakdown pressures are within 65 psi of manually picked values. Breakdown pressure recognition automation is important because it saves time and allows engineers to focus on analytical tasks instead of repetitive data-structuring tasks. Automating this process brings consistency to the data across service providers and basins. In some cases, due to its ability to zoom-in, the algorithm recognized breakdown pressures with higher accuracy than subject matter experts. Comparing the results from two different approaches allowed us to conclude that similar or better results with lower running times can be achieved without using complex algorithms.
Chen, Chi (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Shouxin (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lu, Cong (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Kun (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lai, Jie (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Liu, Yuxuan (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University)
Abstract Hydraulic fracturing technology provides a guarantee for effective production increase and economic exploitation of shale gas wells reservoirs. Propped fractures formed in the formation after fracturing are the key channels for shale gas production. Accurate evaluation of local propped fracture conductivity is of great significance to the effective development of shale gas. Due to the complex lithology and well-developed bedding of shale, the fracture surface morphology after fracturing is rougher than that of sandstone. This roughness will affect the placement of the proppant in the fracture and thus affect the conductivity. At present, fracture conductivity tests in laboratories are generally based on the standard/modified API/ISO method, ignoring the influence of fracture surface roughness. The inability to obtain the rock samples with the same rough morphology to carry out conductivity testing has always been a predicament in the experimental study on propped fracture conductivity. Herein, we propose a new method to reproduce the original fracture surface, and conductivity test samples with uniform surface morphology, consistent mechanical properties were produced. Then, we have carried out experimental research on shale-propped fracture conductivity. The results show that the fracture surfaces produced by the new method are basically the same as the original fracture surfaces, which fully meet the requirements of the conductivity test. The propped fracture conductivity is affected by proppant properties and fracture surface, especially at low proppant concentration. And increasing proppant concentration will help increase the predictability of conductivity. Due to the influence of the roughness of the fracture surface, there may be an optimal proppant concentration under a certain closure pressure.
Selladurai, Jagaan (Petronas Carigali Sdn Bhd) | Roh, Cheol Hwan (Petronas Carigali Sdn Bhd) | Zeidan, Amr (Petronas Carigali Sdn Bhd) | Anand, Saurabh (Petronas Carigali Sdn Bhd) | Madon, Bahrom (Petronas Carigali Sdn Bhd) | Akbar Ali, Anwar Husen (Petronas Carigali Sdn Bhd) | Motaei, Eghbal (Petronas Carigali Sdn Bhd) | Murugesu, Thanapala Singam (Petronas Carigali Sdn Bhd) | Othman, M. Nizar (Petronas Carigali Sdn Bhd) | Ismail, M. Izuddin (Petronas Carigali Sdn Bhd) | Zain, Siti Nur (Petronas Carigali Sdn Bhd) | Zamani, Nur Hidayah (Petronas Carigali Sdn Bhd) | Bela, Sunanda Magna (Petronas Carigali Sdn Bhd)
Abstract Malaysian clastic reservoirs are plagued with high fines content which rapidly deteriorates the productivity from wells completed with conventional form of sand control techniques. To mitigate the fines production issue, Petronas recently successfully completed 3 reservoirs in two wells in Field-D using enhanced gravel pack technique. This paper explains in detail the workflow, challenges such as depleted reservoirs, coal streaks, and nearby water contacts and operational execution for the successful re-defined extension pack jobs. This new approach consists of a re-defined Extension Pack / Frac Pack job with fine movement control resin and a re-defined perforation strategy. Perforation strategy consists of limited number of 180 deg phasing non-oriented perforations done under dynamic underbalance conditions. The key requirement to have fracturing as a sand control method is to have a tip screen out (TSO) or high net pressure placement to ensure the fracture has good conductivity. To obtain a good TSO, data acquisition is of paramount importance. The fracturing jobs in the Field – D wells were preceded with step-rate tests, injection tests, minifrac and Diagnostic Fracture Injection Test (DFIT). The data from diagnostic tests were used diligently to have best possible fracturing treatment in the target zones. Excellent pack factors of greater than 500 lbs. per ft were obtained for all the treatment jobs using only linear gel with proppant concentration up to 7 ppa. This high pack factor translates to very good frac conductivity which is essential in fracturing for sand control. Some of the fracturing treatments concluded with a TSO signature which is a big achievement considering the challenges that were associated with fracturing in Field – D. In addition, DFIT and ACA (After Closure Analysis) was performed to estimate permeability and results were compared with various techniques such as log derived and formation tester permeability. Ultimate objective from this analysis is to have a work-flow which can screen candidate wells for such treatments from openhole logs and give an estimated liquid rate post treatment. Also, the workflow for planning and executing fracturing jobs will be presented for Malaysian clastic reservoirs. This work-flow will be vetted against the extensive diagnostic and fracturing data that has been acquired during fracturing treatments in Field – D. Design, actual diagnostic, and fracturing data will be presented in this paper. It is expected that this modified form of sand and fines control will help in reducing the fines issue in Field – D to a great extent along with expected incremental in oil production. If long term production sustainability is proven, similar approach will be adopted by Petronas and can be shared amongst other South East Asia operators in many similar other fields.