Amount Of Proppant Research Articles

Investigating Proppant Transport in Slickwater Fractures using Direct Numeriacl Simulations (DNS) and Two-Fluid Models (TFM)

Proppant / fracturing fluid two-phase separation always occurs in proppant-slickwater systems, especially in fractures with small width created by low-viscosity slickwater. Forces acting on the proppant include drag from the fluid, body force and force due to inter-particles impede / improve proppant settling. In this paper, lattice Boltzmann method (LBM) using direct numerical simulation (DNS) scheme presented is motivated to resolve lightweight and regular-weight proppant carried by slickwater. DNS simulations have investigated Archimedes number (Ar), proppant volume fraction (ϕ), wall effect (W/dp), horizontal crossflow (Rex) and proppant-slickwater density ratio (ρp/ρf). It is observed that Rex and ρp/ρfhave little effect on proppant settlement as the balance between proppant weight and slickwater viscosity is fixed at a constant Ar. Thus, the proppant carried by slickwater relevant drag correlation series have been derived using small-scale DNS, which is a look-up table that takes fracture aperture, amount of proppant and rheology of fracturing fluid into account. Furthermore, the performances of proppant transport were evaluated by the rate of formation of proppant bank quantitatively and the length and height of proppant bank formed in fractures qualitatively. The drag correlations derived from DNS and Beestra Van der Hoef Kuipers (BVK) were then substituted into MFIX Two Fluid Model (TFM). Proppant bank propagation and equilibrium height in the experiments have validated this DNS derived drag model. Finally, it is to apply DNS-derived drag correlations to characterize proppant settling and transport by slickwater.

Study on Optimization of Stimulation Technology of Heterogeneous Porous Carbonate Reservoir

Mishrif (M) reservoir of Faihaa (F) oilfield in Iraq is a heterogeneous porous carbonate reservoir. The reservoir properties of each reservoir unit differ greatly, and the distribution of porosity and permeability is non-uniform. Some reservoir units have the problem that the expected production cannot be achieved or the production decline rate is too fast after matrix acidification. This work optimized and compared the process of acid fracturing and hydraulic fracturing techniques. The Mishrif B (MB) and Mishrif C (MC) layers are selected as the target units for fracturing and the perforation intervals are optimized. The acid fracturing process adopted the acid fracturing technology of guar gum pad fluid and gelled acid multi-stage injection. According to the wellhead pressure limit and fracture propagation geometry, the pumping rate is optimized. The recommended maximum pumping rate of acid fracturing is 5.0 m3/min, and the optimized acid volume is 256.4 m3. The pressure changes during hydraulic fracturing and acid fracturing are different. It is recommended that the maximum hydraulic fracturing pump rate is 4.5 m3/min for MB and MC layers, and the amount of proppant in MB and MC layers is 37.5 m3 and 43.7 m3, respectively. The production prediction of two optimized processes is carried out. The results showed that the effect of acid fracturing in MB and MC layers is better than hydraulic fracturing, and it is recommended to adopt acid fracturing technology to stimulate MB and MC layers. Acid fracturing operation is carried out in the X-13 well, and better application results are achieved. The results of this study provide optimized reference ideas for reservoir stimulation in heterogeneous porous reservoirs.

Numerical Simulation of Proppant Transport in Major and Branching Fractures Based on CFD-DEM.

This research investigated the effect of branching fracture, proppant, and fracturing fluid on proppant transport based on the CFD-DEM coupling model. The obtained results show that the balance height of embankment in the major fracture decreases gradually with increasing angle between major and branching fractures, while it increases gradually in the branching fracture. This is because the additional resistance of fracturing fluid flow at the joint increases with increasing angle, leading to the decrease of the fracturing fluid velocity. The proppant is prone to settling in branching fractures, resulting in the increase of embankment height in the branching fracture. At angles of 45, 60, and 90°, as the diameter of the proppant increases from 0.8 to 1.1 mm, the balance height of embankment increases slightly in the major fracture, while it decreases in the branching fracture. The frictional resistance of the fracture wall enhances the difficulty of large proppant entering the branching fracture, resulting in a decrease in the amount of proppant entering the branching fracture and a decrease of the balance height of embankment in the branching fracture. In the low-viscosity fracturing fluid, the proppant quickly deposits at the bottom of the fracture as it enters the fracture. Improving the viscosity of the fracturing fluid can significantly enhance its ability to transport the proppant. The proppant is less likely to quickly settle in high-viscosity fracturing fluids, especially when the fracturing fluid viscosity exceeds 50 mPa·s.

Analytical Optimization of Hydraulic Fracturing

Hydraulic fracturing optimization is a critical aspect of improving the recovery of unconventional shale formation. This paper discusses the use of different types of proppants, rate optimization, and proppant amount optimization to improve hydraulic fracturing techniques. The paper begins with a discussion of proppant selection, which is a critical aspect of hydraulic fracturing. The authors highlight the importance of proppant endurance in holding the fracture opening and provide a range of proppants suitable for different confining pressures. Tables and charts are included to illustrate the permeability values of various proppants under different closure stress values. This section also emphasizes the significance of proppant shape in creating a more conductive path in the fracture. The next section of the paper discusses the methodology used in the study, including the Fracpro software simulation parameters. The authors then delve into the optimization of proppant specific gravity and the results of their experiments with five different types of proppants. The paper highlights the impact of proppant specific gravity on fracture width and dimensionless conductivity (FCD). The author also focusses on the optimization of pumping rate, which is an essential parameter of hydraulic fracturing operations. The paper includes simulation studies conducted to determine the effects of pumping rate on fracture parameters such as propped length and propped height. The authors highlight the relationship between rate and FCD and how it is affected by permeability values of the proppant. Finally, the paper discusses proppant amount optimization, which is a critical point of hydraulic fracturing optimization. The authors provide an overview of the results of the experiments conducted to determine the optimal amount of proppant required for different hydraulic fracturing operations. Overall, this paper provides valuable insights for researchers and engineers working to improve hydraulic fracturing techniques for tight shales formation. The authors use a combination of theory, experiments, and charts to provide a comprehensive overview of the various aspects of hydraulic fracturing optimization.

Optimization of fracturing stages/clusters in horizontal well based on unsupervised clustering of bottomhole mechanical specific energy on the bit

Multi-cluster perforation and multi-staged fracturing of horizontal well is one of the main technologies in volumetric fracturing stimulation of unconventional oil and gas reservoirs, but unconventional reservoirs in China are generally of strong heterogeneity, which causes different fracture initiation pressures in different positions of lateral, making it difficult to ensure the balanced fracture initiation and propagation between clusters in multi-cluster perforating. It is in urgent need to precisely evaluate the difference in rock strength in lateral and determine the well section with similar rock strength to deploy fractures, so as to reach the goal of balanced stimulation. Based on the drilling and logging data, this paper establishes an unsupervised clustering model of mechanical specific energy of bit at the bottomhole the lateral. Then, the influence of drill string friction, composite drilling and jet-assisted rock breaking on the mechanical specific energy is analyzed, and the distribution and clustering categories of bottomhole mechanical specific energy with decimeter spatial resolution are obtained. Finally, a fracture deployment optimization method for horizontal well volumetric fracturing aiming balanced stimulation is developed by comprehensively considering inter-fracture interference, casing collar position, plug position, and clustering result of bottomhole mechanical specific energy. The following results are obtained. First, compared with brittleness index, Poisson's ratio and stress difference, perforation erosion area is in a stronger correlation with the mechanical specific energy, and the mechanical specific energy can effectively characterize the difference in the amount of proppant injected into the perforation clusters in the lateral, so it can be served as one of the important indicators for the selection of fracture deployment position. Second, the drilling and logging data cleaning and smoothing and the clustering number selection by the elbow method are the key steps to obtain the clustering results of bottomhole mechanical specific energy, which can tell the difference in the mechanical specific energy with decimeter-level resolution. Third, the interval with mechanical specific energy within 10% of the average value in the section is selected for deploying perforation clusters, and the compiled computer algorithm can automatically determine the optimal position of fracturing section and cluster, so as to realize the differential design of stage spacing and cluster spacing. In conclusion, the research results can further improve the fractures deployment efficiency and balanced stimulation of volumetric fracturing in unconventional oil and gas reservoirs, and this technology is expected to provide ideas and new methods for the fracture deployment optimization of horizontal well volumetric fracturing in unconventional oil and gas reservoirs.

Horizontal well completions using data analytics

There is no quick way to measure completion configurations using simple production data, and assessing them is a challenge for many operators. It has been demonstrated by the O&G industry that completion configurations of horizontal wells influence initial well production potential and long-term performance. The present paper introduces simple, but accurate models to predict completion variables for horizontal wells in the Wolfcamp shale. These include fracture stages, clusters and cluster spacing, and perforations. We believe that these models will help define the optimum completion variables for horizontal wells in unconventional resources. The model development is based on the analysis of hundreds of horizontal wells that include the production history and parameters affecting their production behavior, including but not limited to, well completion configurations, size of proppant, type of fluid, stages, and completed interval of the lateral. We find that six key parameters are essential to precisely predict and optimize the completion variables, namely county, reservoir type, proppant amount, fluid type, rectangular overlap between wells, and initial production. Relationships among these and other parameters and their effect on the production behavior of horizontal wells were evaluated using state-of-the-art data analytics (machine learning). Multivariate linear regression models were devised to predict the four completion variables. Publicly available Well Production Performance data were used as a separate criterion in cross-validating the model predictions out-of-sample. The results of this assessment demonstrate the precision of our models with an absolute relative error of the order of 13%. The true practical advantage of this work is not only in guiding future selective completion variables for horizontal wells in the shale play, but also in providing comparative metrics in assessing different completion styles of various basins using the production history of the offset wells.

Prediction of Longitudinal Superimposed “Sweet Spot” of Tight Gas Reservoir: A Case Study of Block G, Canada

In this paper, taking Block G in Canada as an example, combined with the data of the working area, the Pearson–MIC comprehensive evaluation method was adopted to optimize the key parameters of productivity. Based on the analytic hierarchy process, the weight of each parameter was calculated, the grade of evaluation index of the “sweet spot” was divided, the standard of the sweet spot was established, and the distribution of the superimposed sweet spot was finally depicted. The results show that lateral length, number of stages, volume of fluid, and amount of proppant are the key engineering parameters of horizontal well, and lateral length is an independent key engineering parameter. The cumulative gas production in the first two years was normalized on the lateral length to eliminate the engineering influence, and the total organic carbon (TOC) was finally determined as the key geological parameter, whereas porosity and water saturation were the secondary key parameters. The area of Type I sweet spots accounts for 24.2% in the Series Upper and 23.1% in the Series Lower. This study proposed a new sweet spot prediction idea based on the influence of geological factors on productivity, and its results also laid a foundation for the subsequent placement of horizontal wells in Block G.

Numerical Simulation and Sensitivity Analysis of Hydraulic Fracturing in Multilayered Thin Tight Sandstone Gas Reservoir

Hydraulic fracturing is a necessary measurement to realize the commercial exploitation of oil and gas, but its application in multilayered thin tight sandstone gas reservoirs is still not perfect, which usually have thin gas layers mixed with complex intervals, and shows a dramatic variation in geological and geomechanical properties in the vertical direction. When conventional hydraulic fracturing methods are applied to this kind of reservoir, it is hard to get proper fracture propagation, especially for fracture height control. Facing this situation, this paper proposes a numerical study of the hydraulic fracturing mechanism and analyzes its influencing factors in the multilayered thin tight sandstone gas reservoir. Relying on a real reservoir in the Ordos Basin in China, relevant geological and geomechanical parameters of major gas layers and interlayers are obtained. According to these parameters, the hydraulic fracturing simulation in the multilayered thin tight gas reservoir model is carried out, based on which, the sensitivity analysis of different geological and fracturing parameters which affect the fracture propagation is performed. Furthermore, a real low-production well after fracturing in this kind of reservoir is selected as an example, and based on the analysis, an optimized fracturing scheme is proposed to adapt to the characteristics of the reservoir. According to the comparison of fracturing and production simulations, the optimized fracturing scheme can prevent hydraulic fractures from breaking through thin interlayers, control the fracture height, and prevent fractures from communicating strata with a high water-bearing layer. At the same time, with the same amount of proppant and fracturing fluid, longer fracture length and better fracture conductivity are created, so that the productivity of the optimized fracture has been greatly improved.

Investigation and Application of High-Efficiency Network Fracturing Technology for Deep Shale Gas in the Southern Sichuan Basin.

The Longmaxi Formations in the Luzhou block located in the Southern Sichuan Basin exhibit thick shale formations and huge shale gas resources and have become one of the significant blocks for large-scale production of shale gas. However, due to the natural fractures and high in situ stress and horizontal stress differences, proppants are broken and embedded severely, and complex network fractures are difficult to form, so traditional hydraulic fracturing technology cannot meet the need for profitable development of deep shale gas. In order to increase the stimulated reservoir volume and improve fracture complexity, large-scale hydraulic fracturing experiments and fracture propagation numerical simulations have been conducted based on the geology and engineering treatment difficulty of the Luzhou block to discuss the main factors influencing fracturing effectiveness. Meanwhile, two round field tests were conducted to evaluate the fracturing effectiveness, and the following study results were obtained. First, in situ stress and horizontal stress differences are the main mechanical factors, while cluster spacing and proppant injection intensity are the main fracturing parameters. Therefore, multi-cluster perforation, high-intensity proppant injection, and diversion are employed to improve fracture complexity and conductivity, thus increasing effective fracture volume. Furthermore, the second round of field tests gained remarkable results. The “short-cluster spacing + high proppant amount + variable viscosity slick water + diversion” high-efficiency fracturing technology was formed, and the average test production got to 28.6 × 104 m3/d, which represented a 64% increase over the first round. It concludes that the high-efficiency hydraulic fracturing technology contributes to increasing shale gas production, notably in the Luzhou block for deep shale gas, and provides reliable technology support and study direction for further technical optimization in this block.

Experimental investigation of non-monotonic fracture conductivity evolution in energy georeservoirs

Significant fracture conductivity can be achieved using a much lower material cost based on the optimal partial-monolayer proppant concentration (OPPC) theory. However, experimental validation and investigation of the OPPC theory have been extremely rare in the literature. In this study, we used a laboratory fracture conductivity cell to conduct well-controlled fracture conductivity experiments to comprehensively study the role of effective stress, proppant size, rock type, and water soaking on the evolution of fracture conductivity as a function of increasing proppant concentration. With seven proppant concentrations (up to 2 lb/ft2) and seven effective stresses (up to 6000 psi) used in the conductivity measurements, we experimentally confirmed that the correlation between fracture conductivity and proppant concentration was non-monotonic because of a competing process between fracture permeability and fracture width. We also investigated the influence of the above-mentioned experimental conditions on the OPPC and the corresponding optimal fracture conductivity (OFC). This is the first study that uses well-controlled laboratory experiments to comprehensively investigate non-monotonic fracture conductivity evolutions. The existence of the OPPC indicates that a relatively low proppant amount can be used to form a partial-monolayer proppant pack in the fracture space, which has similar or higher fracture conductivity compared to a multilayer proppant structure. This finding has important economic implications because high-strength, ultralight-weight proppant particles can be used to form partial-monolayer proppant packs in fractures, leading to sufficiently high fracture conductivity using a much lower material cost compared to multilayer proppant structures. Our experiments illustrated that proppant embedment is the primary mechanism that causes the competing process between fracture width and fracture permeability and consequently the non-monotonic fracture conductivity evolution as a function of increasing proppant concentration. Without proppant embedment, there will not be such a competing process, and the non-monotonic fracture conductivity evolution will not be observed.

Machine learning based rate decline prediction in unconventional reservoirs

The main objective of this paper is to develop a novel machine learning based model that can accurately predict the decline curves and EUR (Estimated Ultimate Recovery) for new wells based on the data collected from nearby wells. This is because decline curves are easier and faster alternative to complex reservoir simulators which perform computationally expensive operations. In contrast to this, decline curves require only a few parameters in the equation which can be easily collected from the existing data of the wells. In this study, first we collected the well data corresponding to well parameters such as initial monthly oil flow rate ( q i ), well completion parameters (i.e., no. of fracturing stages, completed length, amount of proppant used, amount of fracturing fluid used), well location parameters (TVD Heel-Toe Difference), reservoir fluid properties (Oil API Gravity, initial 24 h period Gas-Oil Ratio (GOR), initial 24 h period Gas Produced, initial 24 h period Oil Produced), flowing tubing pressure, casing pressure tubing size,choke size from publicly available databases of the Eagle Ford Shale formation Texas RRC (Railroad Commission of Texas). Wells were selected randomly and only those wells were finally included for the study whose data of the all the required parameters were available. The model parameters were estimated by fitting the production data to the decline curve models. Artificial Neural Network (ANN) was employed to build Machine learning models as a function of the above well parameters for the corresponding model parameters. The decline curves for new or existing wells were rapidly predicted using these models. In order to estimate the predictive accuracy of these models when applied to new or test wells cross validation technique such as k-fold cross validation was employed. These models were also used to predict EUR for the test wells. Additionally, feature selection was also done using algorithms such as Chi Square Test (χ2) and Minimum Redundancy Maximum Relevance (MRMR) Algorithm to determine the relative importance of predictor variables in predicting EUR. The predictor variables were successfully linked to SEDM (Stretched Exponential Decline Model) decline curve parameters (n and τ) in a random set of oil field well data. The relative influences of various well parameters were also examined to determine the hidden relationship between them. The novelty in this study lies in the algorithm and dataset that we used for the rate decline prediction in Eagle Ford data set. Although, this paper has referenced some previous papers where machine learning has been used to make prediction, but this paper presents use of new algorithm as well as a new dataset. As more data gets available, there is definitely extra room for further data analysis and improved results using machine learning methods. In future, the number of wells can be increased and the updated results can again be submitted after investing more time. It should be noted here that data downloading and preparing takes most of the time for such study especially when dealing with oil and gas data.

Data-driven multi-objective optimization design method for shale gas fracturing parameters

Accurate design of fracturing parameters in shale gas reservoirs does not only increase the productivity and profitability of wells but also minimize the damage fracturing fluids cause to fluid-sensitive formations. However, obtaining a highly accurate and optimized fracturing parameters with rapid as well cost-effective simulation run is difficult to achieve. This study proposed a framework for optimizing shale gas fracturing parameters based on least squares support vector regression (LSSVR) prediction model and non-dominated sorting genetic algorithm-II (NSGA-II). This framework was named the multi-objective optimization prediction model (MOO-PM). In the proposed framework, the flowback ratio of fracturing fluid (FBR) and first month gas production (PROD) were considered as the objective functions while the fracturing parameters including horizontal length, number of fractured sections, fractured length, fracturing fluid injection rate, fracturing fluid viscosity, volume of fracturing fluid and proppant amount were chosen as the optimization variables. Firstly, based on field data the LSSVR prediction model was established. Secondly, the accuracy of the model was verified by the remaining field data. Subsequently, the Latin Hypercube method was used to construct a sample set of fracturing parameters within a predetermined range of variables and the corresponding objective functions were calculated by the LSSVR method. Finally, Pareto front was used to generate flowback ratio and first-month production by the NSGA-II algorithm. To the best of our knowledge, this is the first time that LSSVR prediction model and multi-objective method are applied simultaneously to optimize shale gas fracturing parameters. Furthermore, the novel multi-objective optimization framework was applied to data from the Weiyuan as well as the Changning shale gas reservoirs in Sichuan basin, southwest China. It was concluded that this framework could optimize fracturing parameters of shale gas reservoirs with higher accuracy and efficiency.

Proppant transport in scaled experiments: Effect of drainage configuration and fracture wall roughness

In recent years, there has been a number of experimental and numerical studies on the transport and settlement of proppant in scaled laboratory fractures that are of interest to understand hydraulic stimulation in hydrocarbon reservoirs. The results from these studies are generally taken with skepticism by the industry. Part of this is due to the perception that results must be very sensitive to the boundary conditions. Small changes in the experimental configuration are expected to lead to very different results. In particular, little is known on how the actual roughness of the fracture walls and the way the slurry is drained from the laboratory cell affect the results. Here, we report experimental results on the proppant transport in a scaled vertical rough cell that mimics a vertical fracture induced during stimulation. We compare results with those obtained in a smooth wall cell, which is more common in laboratory tests. We found that a rough wall allows for a significant increase in the amount of proppant deposited in the cell at high pumping rate. However, the effect of roughness seems to be marginal for low pumping rates, suggesting that most previous studies in the literature which use of a reduced pumping rate were not significantly affected by the use of smooth walls. We also designed different flow configurations at the fracture tip (where the slurry drains form the cell) to study their effect on the way in which the proppant is deposited in the fracture. We found that, in general, the deposited dune is fairly independent of the particular boundary conditions. This suggests that most results reported in the literature are also robust against changes in the selected outflow configuration.

Variation of induced stress field during hydraulic fracture closure and its influence on subsequent fracture

After the completion of multi-stage fracturing operation, the fluid pressure in artificial fracture network continues to decrease, resulting in the closure of fracture and the variation of local induced stress field. This process can also lead to the variation of fracture propagation pattern in the subsequent fracturing stages. To better guide fracturing design, the time effect of stress field was researched. A numerical method was used to solve the induced stress around a propped fracture, and analyze the influence of formation properties and treatment parameters on its change. Then, the characteristics of stress field with different closure times and its influence on subsequent fracture were investigated. Finally, the evolution of the stress field in two stimulated wells was analyzed using the instantaneous shut-in pressure. The results show that the original horizontal stress difference, fluid pressure in fracture, fracture length, fracture angle, and amount of proppant have significant effects on the variation of induced stress field. The stress field of formation with undeveloped natural fractures decreases quickly with the rapid closure of hydraulic fractures due to the rapid drop in fluid pressure, but the stress field remains relatively constant after the fracture is fully propped. For formation with developed low angle natural fractures, the stimulated stage promotes subsequent fracture propagation and improves fracturing efficiency. The fluid pressure declined slowly and is non-uniformly distributed during the closure of the complex fracture network. Therefore, the magnitude and scope of the induced stress vary unevenly with the closure time, and there is a reasonable treatment time to form complex fractures during hydraulic fracturing. This study provides new insights into fracturing operation.

Prediction of fold-of-increase in productivity index post limited entry fracturing using artificial neural network

Unified Fracture Design (UFD) bridges the gap between practices and theory in the hydraulic fracturing industry. It represents a technique to design hydraulic fracturing treatment with a particular amount of proppant. This design could provide the maximum fold-of-increase (FOI) in productivity-index (PI) after hydraulic fracturing treatment. The UFD optimization tool is very effective, but it has assumptions like any other model. One assumption of UFD optimization technique; is a single-layer assumption. This assumption does not align with the limited entry fracturing design concept. In limited entry fracturing, the frictional pressure is employed to offset the stress differences between multi-layers reservoirs to attain fluid injection through these layers, intended to deliver an optimal fracture conductivity in all layers. The drawback of this assumption is the underestimation of the actual value of FOI in PI. This paper aims to recast the original unified fracture design approach to extend the optimal UFD to a multilayer reservoir to predict the FOI in PI after limited entry fracturing treatment. The recasting tool for this problem to find the optimum solution is Artificial Neural Networks (ANN). The architected ANN model is based on actual historical data of limited entry fracturing treatments. A statistical comparison between the proposed ANN model and classical UFD technique demonstrates that ANN model solution has a more reliable estimation of FOI in PI with the actual historical data.

Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation

The production of a single gas well is influenced by many geological and completion factors. The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. Firstly, around 159 horizontal wells were collected, targeting the Duvernay Formation with detailed geological and completion records. Secondly, the key factors were selected using grey relation analysis and Pearson correlation. Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). The model inputs include fluid volume, proppant amount, cluster counts, stage counts, total horizontal lateral length, gas saturation, total organic carbon content, condensate-gas ratio. The model performance was assessed by root mean squared errors (RMSE) and R-squared value. Finally, sensitivity analysis was applied based on best performance model. The analysis shows following conclusions: (1) GPR model shows the best performance with the highest R-squared value and the lowest RMSE. In the testing set, the model shows a R-squared of 0.8 with a RMSE of 280.54 × 104 m3 in the prediction of cumulative gas production within 1st 6 producing months and gives a R-squared of 0.83 with a RMSE of 1884.3 t in the prediction of cumulative oil production within 1st 6 producing months (2) Sensitivity analysis based on GPR model indicates that condensate-gas ratio, fluid volume, and total organic carbon content are the most important features to cumulative oil production within 1st 6 producing months. Fluid volume, Stages, and total organic carbon content are the most significant factors to cumulative gas production within 1st 6 producing months. The analysis progress and results developed in this study will assist companies to build prediction models and figure out which factors control well performance.

Fracture propagation simulation and construction parameter optimization of eruptive phase volcanic rock

A large number of volcanic gas reservoirs are developed in the Sichuan Basin. Up to now, 92 wells have been drilled into Permian volcanic rock, which have good prospects for exploration. Among them, large areas of volcanic facies are distributed in the Sichuan Basin. The uneven distribution of pores in volcanic rocks of eruptive facies and the large difference in rock mechanics combination between different lithofacies make it difficult to develop volcanic gas reservoirs of eruptive facies effectively. Therefore, studying the characteristics of eruptive facies volcanic rocks and reservoir reconstruction schemes is very important for the later development of volcanic rocks Significantly, this paper builds a finite element model of a volcanic rock reservoir of the effusion facies based on the geological characteristics of the volcanic rock of the effusion facies, and optimizes the construction parameters in terms of construction displacement, fracturing fluid volume, and proppant. The research results show that, the injection pressure of the spouting phase under different stress difference conditions exhibits oscillating characteristics. The stress difference between the barrier and the reservoir is positively related to the injection point pressure and fracture fracturing. The recommended construction displacement is 4.5-5.5m3/min and the fracturing fluid dosage is 400-500m3, and the amount of proppant is between 30-40m3. The research content of this article is of great significance for the effective development of volcanic gas reservoirs in eruptive facies.

Data-Mining Approach Evaluates Production Performance in the Permian Basin

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 198192, “Production Performance Evaluation From Stimulation and Completion Parameters in the Permian Basin: Data-Mining Approach,” by Mustafa A. Al-Alwani, SPE, and Shari Dunn-Norman, SPE, Missouri University of Science and Technology, and Larry K. Britt, SPE, NSI Fracturing, et al., prepared for the 2019 SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, 18–19 November, Brisbane, Australia. The paper has not been peer reviewed. The complete paper uses 3,782 unconventional horizontal wells to analyze the effect of proppant volume and the length of the perforated lateral on short- and long-term well productivity across the Permian Basin. Tying cumulative production to completion and stimulation practices showed that increasing pumped proppant per well from 5 million to less than 10 million lbm yielded a 34% increase in 5-year cumulative average barrels of oil equivalent (BOE). Raising the pumped proppant per well to 10 million-15 million lbm and 15 million-20 million lbm increased 5-year cumulative BOE from the previous proppant range group to 27% and 18.5%, respectively. Introduction For this study, stimulation chemical data from Permian (Midland) Basin wells were downloaded from FracFocus for all horizontal wells completed and stimulated between 2012 and 2018. The data were then subjected to rigorous cleaning and processing, a process detailed in the complete paper, and then combined with DrillingInfo completion and production parameters. Combining these data provided ample parameters for stimulation, completion, and production data. The objective of the study was to investigate the production performance of Permian Basin wells as a result of different ranges of stimulation and completion parameters. Fig. 1 shows a database representation of the major counties in the Permian Basin with the number of wells in each county. Results and Discussion To substitute for any quantities of produced gas, all production data have been converted to BOE by using the conversion factor of 1 BOE=6 Mcf. The amount of proppant being pumped and the length of the perforated lateral length have been selected to represent the stimulation size and the completion magnitude, respectively.

A machine learning model for predicting multi-stage horizontal well production

In this study, a hybrid convolutional-recurrent neural network (c-RNN) is evaluated for making predictions of the five-year cumulative production profiles in multistage hydraulically fractured wells. The model was trained by using a combinations of completion parameters, rock mechanical properties, and well spacing and completion order for each stage of 74 wells in the Montney Formation in Alberta. The prediction accuracy of the various combinations was measured by using the mean average percent error and mean absolute error generated through the leave-one-out method. The best combination of inputs was found to be the rock mechanical properties surrounding each perforation cluster, the proppant amount used for every stage, and the spacing and completion order of neighboring wells. The novelty of this study is that the input variables used are at the stage level rather than the average of the entire well. The accuracy of the model was found to increase exponentially as the production of multiple wells was aggregated. The approach yields insights for planning new well drills in fields with existing development since it provides the ability to run multiple field development scenarios without having to spend capital.

Fracture Hits in Unconventional Reservoirs: A Critical Review

Summary“Fracture hit” was initially coined to refer to the phenomenon of an infill-well fracture interacting with an adjacent well during the hydraulic-fracturing process. However, over time, its use has been extended to any type of well interference or interaction in unconventional reservoirs. In this study, an exhaustive literature survey was performed on fracture hits to identify key factors affecting the fracture hits and suggest different strategies to manage fracture hits. The impact of fracture hits is dictated by a complex interplay of petrophysical properties (high-permeability streaks, mineralogy, matrix permeability, natural fractures), geomechanical properties (near-field and far-field stresses, tensile strength, Young’s modulus, Poisson’s ratio), completion parameters (stage length, cluster spacing, pumping rate, fluid and proppant amount), and development decisions (well spacing, well scheduling, fracture sequencing). It is difficult to predict the impact of fracture hits, and they affect both parent and child wells. The impact on the child wells is predominantly negative, whereas the effect on parent wells can be either positive or negative. The “child wells” in this context refer to the wells drilled with pre-existing active/inactive well(s) around. The “parent well” refers to any well drilled without any pre-existing well around. Overall, fracture hits tend to negatively affect both the production and economics of lease development. The optimal approach rests in identifying the reservoir properties and accordingly making field-development decisions that minimize the negative impact of fracture hits. The different strategies proposed to minimize the negative impact of fracture hits are simultaneous lease development, thus avoiding parent/child wells (i.e., rolling-, tank-, and cube-development methods); repressuring or refracturing parent wells; using far-field diverters and high-permeability plugging agents in the child-well fracturing fluid; and optimizing stage and cluster spacing through modeling studies and field tests. Finally, the study concludes with a recommended approach to manage fracture hits. There is no silver bullet, and the problem of fracture hits in each shale play is unique, but by using the available data and published knowledge to understand how fractures propagate downhole, measures can be taken to minimize or even completely avoid fracture hits.