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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
Wu, Tao (CNPC Chuanqing Drilling Engineering Co.LTD) | Fang, Hanzhi (Yangtze University) | Sun, Hu (CNPC Chuanqing Drilling Engineering Co.LTD) | Zhang, Feifei (Yangtze University) | Wang, Xi (Yangtze University) | Wang, Yidi (Yangtze University) | Li, Siyang (Yangtze University)
Abstract Unconventional reservoirs such as shale and tight sandstones that with ultra-low permeability, are becoming increasingly significant in global energy structures (Pejman T, et al., 2017). For these reservoirs, successful hydraulic fracturing is the key to extract the hydrocarbon resources efficiently and economically. However, the intrinsic mechanisms of fracturing growth in the tight formations are still unclear. In practice, fracturing design mainly depends on hypothetical models and previous experience, which leads to difficulties in evaluating the performance of the fracturing jobs. Therefore, an improved method to optimize parameters for fracturing is necessary and beneficial to the industry. In this paper, a data-driven approach is used to evaluate the factors that dominate the production rate from tight sandstone formation in Changqing Field which is the largest oil field in China. In the model, the input parameters are classified into two categories: controllable parameters (e.g. stage numbers, fracturing fluid volume) and uncontrollable parameters (e.g. formation properties), and the output parameter is the accumulated oil production of the wells. Data for more than 100 wells from different formations and zones in Changqing Field are collected for this study. First, a stepwise data mining method is used to identify the correlations between the target parameter and all the available input parameters. Then, a machine learning model is developed to predict the well productivity for a given set of input parameters accurately. The model is validated by using separate data-sets from the same field. An optimize algorithm is combined with the data-driven model to maximize the cumulative oil production for wells by tuning the controllable parameters, which provides the optimized fracturing design. By using the developed model, low productivity wells are identified and new fracturing designs are recommended to improve the well productivity. This paper is useful for understanding the effects of designed fracturing parameters on well productivity in Changqing Oilfield. Furthermore, it can be extended to other unconventional oil fields by training the model with according data sets. The method helps operators to select more effective parameters for fracturing design, and therefore reduce the operation costs for fracturing and improve the oil and gas production.
Summary Stimulated reservoir volume (SRV) is a prime factor controlling well performance in unconventional shale plays. In general, SRV describes the extent of connected conductive fracture networks within the formation. Being a pre-existing weak interface, natural fractures (NFs) are the preferred failure paths. Therefore, the interaction of hydraulic fractures (HFs) and NFs is fundamental to fracture growth in a formation. Field observations of induced fracture systems have suggested complex failure zones occurring in the vicinity of HFs, which makes characterizing the SRV a significant challenge. Thus, this work uses a broad range of subsurface conditions to investigate the near-tip processes and to rank their influences on HF-NF interaction. In this study, a 2D analytical workflow is presented that delineates the potential slip zone (PSZ) induced by a HF. The explicit description of failure modes in the near-tip region explains possible mechanisms of fracture complexity observed in the field. The parametric analysis shows varying influences of HF-NF relative angle, stress state, net pressure, frictional coefficient, and HF length to the NF slip. This work analytically proves that an NF at a 30 ± 5° relative angle to an HF has the highest potential to be reactivated, which dominantly depends on the frictional coefficient of the interface. The spatial extension of the PSZ normal to the HF converges as the fracture propagates away and exhibits asymmetry depending on the relative angle. Then a machine-learning (ML) model [random forest (RF) regression] is built to replicate the physics-based model and statistically investigate parametric influences on NF slips. The ML model finds statistical significance of the predicting features in the order of relative angle between HF and NF, fracture gradient, frictional coefficient of the NF, overpressure index, stress differential, formation depth, and net pressure. The ML result is compared with sensitivity analysis and provides a new perspective on HF-NF interaction using statistical measures. The importance of formation depth on HF-NF interaction is stressed in both the physics-based and data-driven models, thus providing insight for field development of stacked resource plays. The proposed concept of PSZ can be used to measure and compare the intensity of HF-NF interactions at various geological settings.
Luo, Hongwen (Southwest Petroleum University) | Li, Ying (Southwest Petroleum University) | Li, Haitao (Southwest Petroleum University) | Cui, Xiaojiang (Southwest Petroleum University) | Chen, Zhangxin (University of Calgary)
Summary With the increasing application of distributed temperature sensing (DTS) in downhole monitoring for multifractured horizontal wells (MFHWs), well performance interpretation by inversing DTS data has become a popular topic around the world. However, because of the lack of efficient inversion models, great challenges still exist in interpreting flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs from DTS data. In this paper, a robust inversion system is developed to interpret flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs by inversion of DTS data. A temperature prediction model serves as a forward model to simulate the temperature behaviors of MFHWs. A new inversion model based on a simulated annealing (SA) algorithm is proposed to find inversion solutions to flow rate profiles and fracture parameters. The simulated results of temperature behaviors indicate that the temperature profile of each MFHW is irregularly serrated, and the temperature drop in each serration is positively correlated with the inflow rate and fracture halflength. These results provide an excellent method to identify and locate effective hydraulic fractures for field MFHWs. Because of the far more significant influence of fracture half-length than conductivity on a temperature profile, fracture half-length was chosen as the inversion target parameter when performing the inversion of DTS data for MFHWs. Realtime inversion error distributions indicate that this novel inversion system shows great advantages in computational efficiency. Finally, a field application in a shale gas reservoir is presented to validate the reliability of the new inversion model. Based on accurate identification of effective fractures from DTS profiles, satisfactory inversion solutions (the maximum temperature deviation of less than 0.03 K) are obtained. The SA algorithm-based inversion system proves reliable to interpret flow rate profiles and fracture parameters, which is a great help to postfracturing evaluation and productivity improvement for MFHWs in unconventional gas reservoirs. Introduction With the rapid growth of natural gas consumption, the development of unconventional gas (e.g., tight and shale gas) resources has already become a major focus in the oil and gas industry around the world. Because of their poor formation conditions, multifractured horizontal well drilling has become a necessity to improve the productivity and recovery of unconventional gas reservoirs (Yao et al. 2019; Liu et al. 2020). However, the complex downhole conditions created by fracturing treatment make it extremely complicated to diagnose hydraulic fractures and flow rate distributions quantitatively.
Melcher, Howard (Liberty Oilfield Services) | Mayerhofer, Michael (Liberty Oilfield Services) | Agarwal, Karn (Liberty Oilfield Services) | Lolon, Ely (Liberty Oilfield Services) | Oduba, Oladapo (Liberty Oilfield Services) | Murphy, Jessica (Liberty Oilfield Services) | Ellis, Ray (Liberty Oilfield Services) | Fiscus, Kirk (Liberty Oilfield Services) | Shelley, Robert (RF Shelley LLC) | Weijers, Leen (Liberty Oilfield Services)
Summary Selecting appropriate proppants is an important part of hydraulic‐fracture completion design. Proppant selection choices have increased in recent years as regional sands have become the proppant of choice in many liquid‐rich shale plays. But are these new proppants the best long‐term choices to maximize production? Do they provide the best well economics? The paper presents a brief historical perspective on proppant selection followed by various detailed studies of how different proppant types have performed in various unconventional onshore US basins (Williston, Permian, Eagle Ford, and Powder River), along with economic analyses. As the shale revolution pushed into lower‐quality reservoirs, the concept of dimensionless conductivity has pushed our industry to use ever lower‐quality materials—away from ceramics and resin‐coated proppant to white sand in some Rocky Mountain plays, and more recently from white sand to regional sand in the Permian and Eagle Ford plays. Further, we compare early‐to‐late‐time production response and economics in liquid‐rich wells where proppant type changed. The performance of various proppant types and mesh sizes is evaluated using a combination of different techniques, including big‐data multivariate statistics, laboratory‐conductivity testing, detailed fracture and reservoir modeling, as well as direct well‐group comparisons. The results of these techniques are then combined with economic analyses to provide a perspective on proppant‐selection criteria. The comparisons are anchored to permeability estimates from production history matching and diagnostic fracture injection tests (DFITs) and thousands of wellsite‐proppant‐conductivity tests to determine dimensionless conductivity estimates that best approach what is obtained in the field. Dimensionless fracture conductivity is the main driver of well performance because it relates to proppant selection thanks to the inclusion of the relationship of fracture conductivity provided by the proppant relative to the actual flow capacity of the rock (the product of permeability and effective fracture length), which is supported by the production analyses in the paper. The paper shows how much fracture conductivity is adequate for a given effective fracture length and reservoir permeability and then looks at the economics of achieving this “just‐good‐enough” target conductivity, either through less proppant mass with higher‐cost proppants or more proppant mass with lower‐cost proppants, as well as mesh‐size considerations. This paper does not rely on a single technique for proppant selection but uses a combination of various data sources, analysis techniques, and economic criteria to provide a more holistic approach to proppant selection.
Tran, Ngoc Lam (University of Oklahoma) | Gupta, Ishank (University of Oklahoma) | Devegowda, Deepak (University of Oklahoma) | Jayaram, Vikram (Pioneer Natural Resources) | Karami, Hamidreza (University of Oklahoma) | Rai, Chandra (University of Oklahoma) | Sondergeld, Carl H. (University of Oklahoma)
Summary In this study, we demonstrate the application of an interpretable (or explainable) machine‐learning workflow using surface drilling data to identify fracturable, brittle, and productive rock intervals along horizontal laterals in the Marcellus Shale. The results are supported by a thorough model‐agnostic interpretation of the input/output relationships to make the model explainable to users. The methodology described here can easily be generalized to real‐time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production, and minimizing fracture hits. In practice, this information is rarely available in real time and requires tedious and time‐consuming processing of logs (including image logs), core, microseismic data, and fiber‐optic‐sensor data to provide post‐job validation of fracture and well placement. Post‐completion analyses are generally too late for corrective action, leading to wells with a low probability of success and increasing risk of fracture hits. Our workflow involves identifying geomechanical facies from core and well‐log data. We verify that the geomechanical facies derived using core and well‐log data have characteristically different brittleness, fracturability, and production characteristics. We test and investigate several different supervised classifiers to relate surface drilling data to the geomechanical facies. The data were divided into training and test data sets, with supervised classification techniques being able to accurately predict the geomechanical facies with 75% accuracy on the test data set. The clusters predicted on test well (unseen data) were qualitatively verified using the microseismic interpretation. The use of Shapley additive explanations (SHAP) helps explain the predictive models, rank the importance of various inputs in the prediction of the facies, and provides both local and global sensitivities. Our study demonstrates that pre‐existing natural‐fracture networks control both the hydraulic‐fracture geometry as well as the production. Natural fractures promote the formation of complex fracture networks with shorter half‐lengths, which increase well productivity while minimizing fracture hits and neighboring‐well interactions. The natural‐fracture network is itself controlled by the geomechanical properties of the rock. The ability of the surface drilling data to reliably predict the geomechanical rock facies provides a powerful tool for real‐time optimization of wellbore trajectory and completions.
Unconventional reservoirs, such as Permian Basin, have fundamentally different production behaviors than that of the conventional reservoirs because of the low permeability of formations away from the stimulated volume. Thus, it is difficult to run full-field reservoir simulation to generate a full-field development plan. Even though satisfactory history matching for completion and production data can be achieved for wells at one location, it is difficult to directly apply the results to other areas in the same region. This is especially true in complex thick pay zone reservoirs, such as Permian Basin, where the complex geology, geomechanical properties, and resource properties all make the solution of frac hit (optimal well spacing) very challenging. To our best knowledge, this study is the first to integrate the detailed physics-based simulation, including fracturing simulation, coupled reservoir and geomechanics simulation, with machine learning (ML) to generate a sound workflow for well spacing optimization. In this workflow, a large field is first divided into several representative regions according to geology, geomechanical properties, and reservoir properties; and a typical well is selected for each region. High-quality physics simulation (including fracturing simulation, coupled reservoir simulation and geomechanics simulation) and history matching are then performed for a pair of parent and child wells. Additional well completion scenarios are built upon the base case, which serve as input to the following ML study. Various ensemble regression methods are applied to generate production predictions for unexplored reservoir locations in this field.
In recent years, many fracture simulation models have been developed to represent the complex geomechanical processes involved in hydraulic fracturing (F. Ajisafe, Shan, Alimahomed, Lati, & Ejofodomi, 2017; Morales et al., 2016; Pankaj, 2018b). Among them, well interference or well spacing optimization is a critical issue to solve in energy sector, especially during the current industrial downturn (F. O. Ajisafe, Solovyeva, Morales, Ejofodomi, & Porcu, 2017; Pankaj, 2018a; L. Wang & Yu, 2019; M. Wang, Wang, Zhou, & Yu, 2019). Detailed mechanistic study of integrated fracturing simulation and reservoir simulation have made significant progress during the recent years, which greatly helps to unlock unconventional resources and assist the industry to achieve economic goals (Alimahomed et al., 2017; Lashgari, Sun, Zhang, Pope, & Lake, 2019; Min, Sen, Ji, & Sullivan, 2018; Rodriguez, 2019). Nevertheless, when applying these technologies in unconventional field development and production, major uncertainties remain, including geology aspects such as stress orientation, stress anisotropy and natural fracture distribution, and completion aspects such as discrepancy between different completion strategies (Pankaj, Shukla, Kavousi, & Carr, 2018; Xiong, Liu, Feng, Liu, & Yue, 2019). A common concern is that even though current physics-based modeling and simulation could match the completion and historical production of a single well or multiple wells, it is still difficult to successfully transfer or scale up one small-area’s knowledge and experience to another area, because of the complexity and uncertainty of unconventional reservoirs (Xiong, Ramanathan, & Nguyen, 2019; Yeh et al., 2018).
Pei, Yanli (The University of Texas at Austin) | Wang, Jiacheng (The University of Texas at Austin) | Yu, Wei (The University of Texas at Austin \ Sim Tech LLC. ) | Sepehrnoori, Kamy (The University of Texas at Austin)
The existence of natural fractures in tight reservoirs causes great uncertainty in the infill-well completion. However, it is difficult to quantify the effects of natural fractures on stress evolution and frac hits due to the stochastic manner of the natural fracture system. In this work, we investigate the impact of two types of lateral complexity, i.e., parallel fracture complexity and transverse fracture complexity, on the stress redistribution and propose suggestions to mitigate frac hits during interwell fracturing. An in-house 3D coupled geomechanics and compositional simulator is used to model the pressure and stress distribution, followed by a displacement discontinuity method hydraulic fracture model to simulate the infill-well fracture propagation. Numerical results show that the parallel natural fracture serves as an attractor of stress reorientation, whereas the transverse natural fracture acts as a disperser of the orientation change. The existence of the parallel fracture complexity induces significant local stress heterogeneity around the natural fracture tip; thus, parallel rather than staggered perforation of the infill-well is favored to reduce the risk of frac hits. Since the transverse fracture complexity leads to more uniform stress reorientation between parent-well and infill-well, the perforation location is not as important as that in the parallel complexity case. By uncovering the fundamental mechanisms of lateral fracture complexity on frac hits, this work provides some insights into the interwell fracturing of tight reservoirs and will facilitate the on-site hydrocarbon production.
Field observations have shown the complex growth of hydraulic fractures in both lateral and vertical directions (Cipolla et al., 2008; Soliman et al. 2010; Wilson, 2015). The fracture network complexity is generally induced by the interaction between hydraulic fractures and pre-existing natural fractures, fissures, or faults (Cruz et al., 2017; Wang et al., 2020). One possible situation is that the hydraulic fractures propagate in parallel with microfractures (Nguyen and Fleming, 2012; Sierra, 2016), as shown in
Dalamarinis, Panagiotis (Seismos) | Mueller, Paul (Mueller Energy Consulting) | Logan, Dale (NexTier Completion Solutions) | Glascock, Jason (NexTier Completion Solutions) | Broll, Stephen (VirTex Operating)
This paper assesses the effectiveness of combining hydraulic fracture monitoring (performed using borehole pressure-wave readings) with facies analysis based on mechanical specific energy (MSE) measurements. Beneficial applications include: 1) evaluation and optimization of completion designs, 2) design and measurement of diversion effectiveness and 3) placement of the frac as designed – while avoiding offset well communication – to increase estimated ultimate recovery (EUR). The evaluation was performed on a four-well dataset in the Eagle Ford shale.
For each well, facies analysis directed pre-job planning, resulting in various frac stage designs that were based on variations in MSE. The stages were monitored during the job, and, based on results, frac stage designs were modified in real time to optimize the next geomechanically similar stage. Far-field diversion was used on targeted stages to limit half-length growth in select wells. On all the wells, the number of clusters per stage was varied and the impact was monitored.
The first well was used as a baseline to provide direct, quantifiable correlations between the facies MSE and the measured fracture half-lengths. On subsequent wells, different treatment designs were executed, based on the varying MSE measurements, to obtain the desired half-length. The design changes included variations in the number of clusters per stage, far-field diversion strategies, pump rates, and proppant concentrations and quantities. Throughout the operation, frac performance was monitored continuously and pumping designs were optimized by varying parameters such as perforation clusters spacing, pump rate, diverter, acid volume, pad volume, slurry/proppant design, and volume per linear foot. The completion design of every stage was modified in real time, based on the performance of the fracture system. In each well, the first stages in each rock type served as control stages for calibration purposes. The result was the development of a uniform fracture system, in terms of both its extension as well as its near- and far-field conductivity. In a series of 204 stages across all four wells, the integration of MSE facies with fracture performance enabled real-time optimization of the fracture system, which delivered significant improvements in production performance, reservoir development, and reduced rate of depletion.
The combination of MSE analysis with borehole pressure-wave-based hydraulic fracture monitoring is a paradigm shift that has the potential to revolutionize how horizontal plays are developed. Employing these combined technologies can be used to drive each frac stage to meet frac half-length, height, and conductivity goals. The fit-for-purpose, noninvasive and scalable qualities of both technologies deliver strong cost efficiencies and can significantly increase EUR from the project acreage. At both the well and field levels, this combination of cost efficiency and customizability is critical to optimizing recovery from the field and increasing the economic life of industrialized shale completions.
In this study a new semi-analytical model is introduced for fracture estimation with pressure transient analysis in "FS" process. Multiple mechanisms are considered in the model development, including complex fracture networks, fracture propagation, and fracture closure. Using methods of boundary element method, Laplace transforms, and superposition principle, we obtain its pressure transient solution. Then, the semi-analytical model is verified by using a simplified case. Finally, based on "FS" data of wells in the Jimusar Sag, it is applied to conduct type-curve matching to determine their fracture parameters.
Results show that flow regimes of shale-oil fractured wells during "FS" process have three special stages, containing before-closure, after-closure, skin effect. The pressure behaviors of "FS" process are similar to the phenomenon of changing wellbore storage. A case with a vertically fractured well in the literature is studied to compare the results from this work and those in the literature for model verification. After comparison, it is found that the results from this work match well with those from the work in the literature. Results from the field application show that the number of hydraulic fracture is 3∼4 per stage, and the number of reactivated natural fracture is ranging from 2∼5 at each fracture wing. The hydraulic fracture conductivity is approximated to be infinite, as the "FS" testing is in the early time after fracturing treatment, while the natural fracture conductivity ranges between 2 mD·m and 5 mD·m. This study may provide reservoir engineers a new diagnostic tool to analyze reservoirs characteristics for "fracturing-shutting" with fracture propagation and fracture closure.
The increasing global energy demand coupled with the dwindling hydrocarbon supply from conventional resources has shifted the attention of unconventional resources. Recently, a combination of horizontal well technology and hydraulic fracturing has made exploitation of these unconventional shale oil reservoirs implementable. As most of unconventional resources suffer from low porosity and ultra-low permeability, which is in the micro-Darcy range, requiring hydraulic fracturing stimulation to create flow paths and allow the hydrocarbons to be produced. For unconventional reservoirs (shale, tight, etc.), the degree of fracture development is the most important concern in reservoir evaluation.