Several recent studies have reported that proppant "bridging" (blocking) occurs at the interface between primary and secondary fractures. Such bridging blocks flow and significantly reduces the efficiency of proppant placement. The prevention of bridging is of great importance, but the criteria for bridging formation have yet to be determined. In this numerical study of proppant transport, we propose bridging formation criteria and analyze the associated distribution correlations that quantify the amount of proppant that migrates into the secondary fractures. To model the complex interactions between proppant particles, fracturing fluids, and fracture walls, we use the discrete element method (DEM) coupled with computational fluid dynamics (CFD). We calibrate our model using widely accepted bed-load transport measurements. The simulation domain involves a "T-type" intersection of primary and secondary fractures. We investigate the effects of various proppant sizes and concentrations on bridging formation. In all cases, we investigate the occurrence of bridging and we quantify its impact by estimating the corresponding percentage of proppant reaching the secondary fractures. Our simulation results show that the efficiency of proppant placement in the secondary fractures depends on the flow regime. In the suspension regime, proppant particles can be easily mobilized by the fluid drag force. This leads to a relative high proppant placement efficiency in the secondary fractures. When proppants are in the bed-load transport regime, kinetic energy transferred from the fluid drag force is dissipated by inter-particle collisions and the friction force. In this case, the amount of proppants entering the secondary fractures and the distance that proppants can cover are restricted compared to the case of proppants associated with suspension transport. Our investigation reveals that two parameters are critical for the occurrence of proppant bridging (blocking) at the secondary fracture interface. These parameters are — the proppant concentration Cp and the ratio between the secondary fracture aperture and the proppant diameter (Rfp). At a fixed value of Rfp, continuous transport of proppant can be achieved when Cp is lower than a threshold value. Based on this finding, we use Rfp and Cp to propose a blocking criterion correlation.
{"title":"Bridging Criteria and Distribution Correlation for Proppant Transport in Primary and Secondary Fracture","authors":"Rui Kou, G. Moridis, T. Blasingame","doi":"10.2118/194319-MS","DOIUrl":"https://doi.org/10.2118/194319-MS","url":null,"abstract":"\u0000 Several recent studies have reported that proppant \"bridging\" (blocking) occurs at the interface between primary and secondary fractures. Such bridging blocks flow and significantly reduces the efficiency of proppant placement. The prevention of bridging is of great importance, but the criteria for bridging formation have yet to be determined. In this numerical study of proppant transport, we propose bridging formation criteria and analyze the associated distribution correlations that quantify the amount of proppant that migrates into the secondary fractures.\u0000 To model the complex interactions between proppant particles, fracturing fluids, and fracture walls, we use the discrete element method (DEM) coupled with computational fluid dynamics (CFD). We calibrate our model using widely accepted bed-load transport measurements. The simulation domain involves a \"T-type\" intersection of primary and secondary fractures. We investigate the effects of various proppant sizes and concentrations on bridging formation. In all cases, we investigate the occurrence of bridging and we quantify its impact by estimating the corresponding percentage of proppant reaching the secondary fractures.\u0000 Our simulation results show that the efficiency of proppant placement in the secondary fractures depends on the flow regime. In the suspension regime, proppant particles can be easily mobilized by the fluid drag force. This leads to a relative high proppant placement efficiency in the secondary fractures. When proppants are in the bed-load transport regime, kinetic energy transferred from the fluid drag force is dissipated by inter-particle collisions and the friction force. In this case, the amount of proppants entering the secondary fractures and the distance that proppants can cover are restricted compared to the case of proppants associated with suspension transport.\u0000 Our investigation reveals that two parameters are critical for the occurrence of proppant bridging (blocking) at the secondary fracture interface. These parameters are — the proppant concentration Cp and the ratio between the secondary fracture aperture and the proppant diameter (Rfp). At a fixed value of Rfp, continuous transport of proppant can be achieved when Cp is lower than a threshold value. Based on this finding, we use Rfp and Cp to propose a blocking criterion correlation.","PeriodicalId":103693,"journal":{"name":"Day 2 Wed, February 06, 2019","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131123648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Das, O. Solaja, J. Cabrera, R. Scofield, E. Coenen, S. Kashikar, Charles Kahn
The industry continues to face a challenge understanding and optimizing completion strategies to minimize the impact of infill development on existing wells and achieve larger Stimulated Reservoir Volume (SRV) on infill wells. This paper presents a cost-effective technique for evaluating parent-child interaction using poroelastic pressure responses on the parent wells. The method was employed on a four-well pad in the Eagle Ford to understand diversion effectiveness and the extent of offset depletion. The case study comprised the analysis of pressure data sets, covering wellhead pressure data from the nearby parent wells. The method quantifies and interprets pressure signal magnitude and its transient behavior for each completed stage. The well offsetting the parent well was completed using two different completion designs. One half of the lateral was completed without employing diversion, while the other half employed a specific diversion strategy. The primary goal of the case study was to demarcate the areal extent and degree of depletion around the existing wells and determine the effectiveness of using diversion in inhibiting growth towards parent wells. The analysis determined fluid and fracture pathways, mainly seen driven by formation stresses, depletion, and completion design in each stage. The case study compared the effects of employing diversion vs. not employing diversion, using the magnitude of pressure responses felt by the parent well. The initiation points of the pressure signals, as felt by the offset wells on each side quantified how quickly and in which direction the newly treated fractures were growing. The pressure responses from multiple parent wells were correlated to understand the areal extent of depletion around each offset producer. This ultimately promotes understanding the difference in pre and post production of the wells and optimizes infill completions for future development. Cross well analysis using poroelastic pressure responses is easy to implement and very cost-effective. The proposed method provides a workflow to analyze offset pressure data in a consistent and reproducible manner. This method affords the industry a better understanding of parent well damage and mitigation of child well productivity loss.
{"title":"Child Well Analysis from Poroelastic Pressure Responses on Parent Wells in the Eagle Ford","authors":"P. Das, O. Solaja, J. Cabrera, R. Scofield, E. Coenen, S. Kashikar, Charles Kahn","doi":"10.2118/194354-MS","DOIUrl":"https://doi.org/10.2118/194354-MS","url":null,"abstract":"\u0000 The industry continues to face a challenge understanding and optimizing completion strategies to minimize the impact of infill development on existing wells and achieve larger Stimulated Reservoir Volume (SRV) on infill wells. This paper presents a cost-effective technique for evaluating parent-child interaction using poroelastic pressure responses on the parent wells. The method was employed on a four-well pad in the Eagle Ford to understand diversion effectiveness and the extent of offset depletion.\u0000 The case study comprised the analysis of pressure data sets, covering wellhead pressure data from the nearby parent wells. The method quantifies and interprets pressure signal magnitude and its transient behavior for each completed stage. The well offsetting the parent well was completed using two different completion designs. One half of the lateral was completed without employing diversion, while the other half employed a specific diversion strategy. The primary goal of the case study was to demarcate the areal extent and degree of depletion around the existing wells and determine the effectiveness of using diversion in inhibiting growth towards parent wells.\u0000 The analysis determined fluid and fracture pathways, mainly seen driven by formation stresses, depletion, and completion design in each stage. The case study compared the effects of employing diversion vs. not employing diversion, using the magnitude of pressure responses felt by the parent well. The initiation points of the pressure signals, as felt by the offset wells on each side quantified how quickly and in which direction the newly treated fractures were growing. The pressure responses from multiple parent wells were correlated to understand the areal extent of depletion around each offset producer. This ultimately promotes understanding the difference in pre and post production of the wells and optimizes infill completions for future development.\u0000 Cross well analysis using poroelastic pressure responses is easy to implement and very cost-effective. The proposed method provides a workflow to analyze offset pressure data in a consistent and reproducible manner. This method affords the industry a better understanding of parent well damage and mitigation of child well productivity loss.","PeriodicalId":103693,"journal":{"name":"Day 2 Wed, February 06, 2019","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126898306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robert David Barree, Robert Duenckel, Barry Hlidek
Two primary criteria describe proppants utilized in fracturing: type (e.g. - sand) and mesh size (e.g. - 30/50), where mesh size refers to the number of wires per inch in the standard U. S. sieve screens. For a proppant to meet API RP-19C (API, 2006) specifications, 90% of the material sample (by weight) must fall between the screens of the largest and smallest specified mesh size. These size specifications provide the user of proppants a method of choosing a proppant, and comparing products from different suppliers, but still allows a wide variance in particle size within each sieve distribution. Laboratory conductivity tests demonstrate that limiting sieve distribution to standard sizing per API specifications is not a requirement to obtain adequate conductivity performance, or a sufficient descriptor of proppant performance. The industry has for the most part, limited its choices of proppants to API sizing criteria. It should be noted however, that within each standard mesh range (40/70, 30/50, 20/40, etc.) there is allowed a doubling of size from the smallest to largest particle diameter. There can be a significant difference in size distribution and performance between two proppants, both of which meet the API specification for a given mesh distribution. The difference in distribution can be recognized by determining the median particle diameter of the proppant sample. API RP-19C defines the median diameter as the fiftieth mass percentile (d50) in the distribution. Thousands of conductivity tests have demonstrated a very strong correlation between median particle diameter and conductivity for each specific type of proppant. The correlation provides a methodology of predicting the conductivity of differing mesh distributions within a specific standard mesh size designation, or for mixed distributions of various particle sizes. This correlation can be successfully applied to regional, non-standard, sand samples. The ramification of the correlation of median particle diameter to conductivity suggests that standard mesh distributions are somewhat arbitrary and that using non-standard size distributions is not necessarily a negative. Recognizing that sieving capacity is often a bottleneck to output, choosing to provide non-standard sizing may lead to greater production for a processing facility. Given potential proppant supply constraints in the industry, such a shift in proppant supply may lead to significantly improved sand availability and cost benefits to operators.
压裂过程中使用的支撑剂有两个主要标准：类型（如砂）和网目尺寸（如 30/50），其中网目尺寸是指美国标准筛网中每英寸的筛丝数量。要使支撑剂符合 API RP-19C（API，2006 年）的规格要求，90% 的材料样品（按重量计）必须位于最大和最小的指定筛孔之间。这些粒度规格为支撑剂用户提供了选择支撑剂和比较不同供应商产品的方法，但仍允许每个筛孔分布内的粒度存在较大差异。实验室电导率测试表明，将筛孔分布限制在 API 规范规定的标准粒度范围内，并不是获得足够电导率性能的必要条件，也不是支撑剂性能的充分描述指标。在大多数情况下，该行业对支撑剂的选择都局限于 API 尺寸标准。但需要注意的是，在每个标准目数范围内（40/70、30/50、20/40 等），从最小颗粒直径到最大颗粒直径的尺寸允许增加一倍。如果两种支撑剂的粒度分布和性能都符合 API 对特定目数分布的规定，那么它们之间的粒度分布和性能可能会有很大差异。可以通过确定支撑剂样品的中值粒径来识别粒度分布的差异。API RP-19C 将中值直径定义为分布中的第 50 个质量百分位数 (d50)。数以千计的电导率测试表明，对于每种特定类型的支撑剂，颗粒直径中值与电导率之间都存在很强的相关性。这种相关性提供了一种方法，可用于预测特定标准粒径指定范围内不同网孔分布或各种粒径混合分布的电导率。这种相关性可成功应用于区域性非标准砂样本。颗粒直径中值与电导率的相关性表明，标准网目分布在某种程度上是任意的，使用非标准粒度分布并不一定是坏事。由于筛分能力往往是产量的瓶颈，因此选择提供非标准粒度可能会提高加工设施的产量。考虑到行业中潜在的支撑剂供应限制，支撑剂供应的这种转变可能会大大提高砂的可用性，并为运营商带来成本效益。
{"title":"Proppant Sieve Distribution - What Really Matters?","authors":"Robert David Barree, Robert Duenckel, Barry Hlidek","doi":"10.2118/194382-MS","DOIUrl":"https://doi.org/10.2118/194382-MS","url":null,"abstract":"\u0000 Two primary criteria describe proppants utilized in fracturing: type (e.g. - sand) and mesh size (e.g. - 30/50), where mesh size refers to the number of wires per inch in the standard U. S. sieve screens. For a proppant to meet API RP-19C (API, 2006) specifications, 90% of the material sample (by weight) must fall between the screens of the largest and smallest specified mesh size. These size specifications provide the user of proppants a method of choosing a proppant, and comparing products from different suppliers, but still allows a wide variance in particle size within each sieve distribution. Laboratory conductivity tests demonstrate that limiting sieve distribution to standard sizing per API specifications is not a requirement to obtain adequate conductivity performance, or a sufficient descriptor of proppant performance.\u0000 The industry has for the most part, limited its choices of proppants to API sizing criteria. It should be noted however, that within each standard mesh range (40/70, 30/50, 20/40, etc.) there is allowed a doubling of size from the smallest to largest particle diameter. There can be a significant difference in size distribution and performance between two proppants, both of which meet the API specification for a given mesh distribution. The difference in distribution can be recognized by determining the median particle diameter of the proppant sample. API RP-19C defines the median diameter as the fiftieth mass percentile (d50) in the distribution.\u0000 Thousands of conductivity tests have demonstrated a very strong correlation between median particle diameter and conductivity for each specific type of proppant. The correlation provides a methodology of predicting the conductivity of differing mesh distributions within a specific standard mesh size designation, or for mixed distributions of various particle sizes. This correlation can be successfully applied to regional, non-standard, sand samples.\u0000 The ramification of the correlation of median particle diameter to conductivity suggests that standard mesh distributions are somewhat arbitrary and that using non-standard size distributions is not necessarily a negative. Recognizing that sieving capacity is often a bottleneck to output, choosing to provide non-standard sizing may lead to greater production for a processing facility. Given potential proppant supply constraints in the industry, such a shift in proppant supply may lead to significantly improved sand availability and cost benefits to operators.","PeriodicalId":103693,"journal":{"name":"Day 2 Wed, February 06, 2019","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126675714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In some basins, large scale development of unconventional stacked-target plays requires early election of well targeting and spacing. Changes to the initial well construction framework can take years to implement due to lead times for land, permitting, and corporate planning. Over time, as operators wish to fine tune their development plans, completion design flexibility represents a powerful force for optimization. Hydraulic fracturing treatment plans may be adjusted and customized close to the time of investment. With a practical approach that takes advantage of physics-based modeling and data analysis, we demonstrate how to create a high-confidence, integrated well spacing and completion design strategy for both frontier and mature field development. The Dynamic Stimulated Reservoir Volume (DSRV) workflow forms the backbone of the physics-based approach, constraining simulations against treatment, flow-back, production, and pressure-buildup (PBU) data. Depending on the amount of input data available and mechanisms investigated, one can invoke various levels of rigor in coupling geomechanics and fluid flow – ranging from proxies to full iterative coupling. To answer spacing and completions questions in the Denver Basin, also known as the Denver-Julesburg (DJ) Basin, we extend this modeling workflow to multi-well, multi-target, and multi-variate space. With proper calibration, we are able generate production performance predictions across the field for a range of subsurface, well spacing, and completion scenarios. Results allow us to co-optimize well spacing and completion size for this multi-layer column. Insights about the impacts of geology and reservoir conditions highlight the potential for design customization across the play. Results are further validated against actual data using an elegant multi-well surveillance technique that better illuminates design space. Several elements of subsurface characterization potentially impact the interactions among design variables. In particular, reservoir fluid property variations create important effects during injection and production. Also, both data analysis and modeling support a key relationship involving well spacing and the efficient creation of stimulated reservoir volumes. This relationship provides a lever that can be utilized to improve value based on corporate needs and commodity price. We introduce these observations to be further tested in the field and models.
{"title":"Right-Sized Completions: Data and Physics-Based Design for Stacked Pay Horizontal Well Development","authors":"K. V. Tanner, W. Dobbs, Steven D. Nash","doi":"10.2118/194312-MS","DOIUrl":"https://doi.org/10.2118/194312-MS","url":null,"abstract":"\u0000 In some basins, large scale development of unconventional stacked-target plays requires early election of well targeting and spacing. Changes to the initial well construction framework can take years to implement due to lead times for land, permitting, and corporate planning. Over time, as operators wish to fine tune their development plans, completion design flexibility represents a powerful force for optimization. Hydraulic fracturing treatment plans may be adjusted and customized close to the time of investment.\u0000 With a practical approach that takes advantage of physics-based modeling and data analysis, we demonstrate how to create a high-confidence, integrated well spacing and completion design strategy for both frontier and mature field development. The Dynamic Stimulated Reservoir Volume (DSRV) workflow forms the backbone of the physics-based approach, constraining simulations against treatment, flow-back, production, and pressure-buildup (PBU) data. Depending on the amount of input data available and mechanisms investigated, one can invoke various levels of rigor in coupling geomechanics and fluid flow – ranging from proxies to full iterative coupling.\u0000 To answer spacing and completions questions in the Denver Basin, also known as the Denver-Julesburg (DJ) Basin, we extend this modeling workflow to multi-well, multi-target, and multi-variate space. With proper calibration, we are able generate production performance predictions across the field for a range of subsurface, well spacing, and completion scenarios. Results allow us to co-optimize well spacing and completion size for this multi-layer column. Insights about the impacts of geology and reservoir conditions highlight the potential for design customization across the play. Results are further validated against actual data using an elegant multi-well surveillance technique that better illuminates design space.\u0000 Several elements of subsurface characterization potentially impact the interactions among design variables. In particular, reservoir fluid property variations create important effects during injection and production. Also, both data analysis and modeling support a key relationship involving well spacing and the efficient creation of stimulated reservoir volumes. This relationship provides a lever that can be utilized to improve value based on corporate needs and commodity price. We introduce these observations to be further tested in the field and models.","PeriodicalId":103693,"journal":{"name":"Day 2 Wed, February 06, 2019","volume":"65 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132937172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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