Day 2 Wed, February 02, 2022最新文献

{"title":"A New Crack Propagation Algorithm That Enables Accurate Simulation of Propagation Across Thin Layers in a Practical Field-Scale Fracturing Model","authors":"E. Dontsov, C. Hewson, M. McClure","doi":"10.2118/209146-ms","DOIUrl":"https://doi.org/10.2118/209146-ms","url":null,"abstract":"\u0000 We developed a propagation algorithm that enables accurate calculation of hydraulic fracture geometry, even in the presence of thin layers and with a relatively coarse crack mesh. Conventionally, hydraulic fracture simulators either add one element at a time (less accurate) or require constant remeshing (computationally expensive). Our new algorithm benefits from the simplicity of adding one element at a time, but does not compromise the accuracy. This is because the algorithm continuously tracks the position of the crack tip within each tip element. In addition, the continuous front tracking allows for the algorithm to account for varying rock properties within the element and to accurately simulate propagation across arbitrarily thin layers. The algorithm has been implemented in a commercial combined hydraulic fracturing and reservoir simulator. First, some mathematical foundations of the algorithm are outlined. Then, we perform basic validation simulations, showing that the algorithm can accurately solve standard benchmark solutions in the toughness and viscosity dominated regimes. Next, we simulate propagation of a single crack in a layered media with highly variable properties (e.g. stress or toughness) between layers. We perform sensitivity analysis to show that the algorithm retains accuracy, even if the mesh is coarsened considerably. The mesh coarsening leads to dramatically faster numerical simulations, relative to the corresponding fine mesh solutions. Finally, we apply the algorithm in a field-scale simulation of an actual pad-scale dataset. We show that the algorithm is capable of accurately reproducing the fracture geometry and, at the same time, retains practical computational times.","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125514624","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}

{"title":"Modeling Propped and Unpropped Fracture Conductivity in Fracture and Reservoir Models: Powder River Basin Turner Study","authors":"Han Li, Jiasen Tan, D. P. Craig, A. Brehm, Jichao Han, Qian Li","doi":"10.2118/209188-ms","DOIUrl":"https://doi.org/10.2118/209188-ms","url":null,"abstract":"\u0000 Well spacing in unconventional fields is a challenging task because the hydraulic fracture geometry is not fully known and there is uncertainty about reservoir properties. Integrating geomechanics medeling with reservoir simulation is a cost-effective and efficient approach for generating realistic fracture geometry, estimating fracture conductivity, and understanding fluid flow behavior. This paper presents a case study of integrated geomechanical and reservoir flow simulation validated with fracture and reservoir diagnostics for the purpose of determining ideal well spacing.\u0000 In a Rocky Mountain Powder River Basin field, a standalone horizontal well in the Turner reservoir was produced for almost a year before drilling adjacent infill producers. FMI logs recorded in the infill wells indicated hundreds of hydraulic fractures growing from the parent well. Based on the distances and the hydraulic fracture density from the image logs, a 3D fracture model was calibrated for the parent well.\u0000 The fracture geometry and simulated proppant concentration were then exported to a reservoir simulator, where production was history matched. This simulation was constrained by measured far-field pressure data from the DFIT in each wellbore. A rigorous fracture conductivity calculation workflow was developed and applied before reservoir simulation. The conductivity workflow uses simulated proppant concentration from the fracture model and conductivity measurements of propped and unpropped fractures to define a conductivity distribution for reservoir simulation. Additionally, a proppant creep function was developed to characterize proppant-pack conductivity degradation over time.\u0000 By applying the fracture conductivity calculation workflow and the measured far-field pressure constraints, the production history was matched for the full parent wellbore. The simulation results show that the parent well's drainage are covers some of the infill well's targeted reservoir. Subsequent hydraulic fracture simulations of multiple infill wells then demonstrated the effect of parent well depletion and rock stresses on the hydraulic fracture geometry of the infill wells.\u0000 Our case study illustrates: (a) how modeled hydraulic fracture geometry can be calibrated using image logs recorded in the laterals of infill wells, (b) how a new proppant conductivity algorithm can be used to assign propped and unpropped fracture conductivity based on physics-based models of proppant concentration, (c) how reservoir simulation can be constrained with a calibrated fracture model and far-field pressure measurements, and (d) how a new proppant creep function can be used to describe proppant-pack conductivity degradation over time.","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116035243","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}

{"title":"A First Step for Fracturing: Successful Real Time Brain Calibration Lays the Foundation for Predicting Fracturing Outcomes","authors":"Mahadev Karthik, Kuesters Andrea, Czeropski Andrew, Hu Yue, Vergara Juan","doi":"10.2118/209134-ms","DOIUrl":"https://doi.org/10.2118/209134-ms","url":null,"abstract":"\u0000 Fracturing utilizes significant heuristics and biases in their execution. A pressure history match, changing simulator inputs to match actual field test, is limited by rig time and capacity of a human brain. A virtual agent or brain can quickly iterate across small step sizes for a wide range of inputs to find optimal real time solutions. The ability to match pressures in real time will allow time and cost savings on minifrac tests and reverse outs. More importantly, the virtual brain can learn from past mistakes, get better over time (reinforcement learning), never forget past tests, and compare pressure signatures from vast historical data. This would be the foundational step for predictive ability on frac treatments that would eventually automate operational decision making. This also allows for consistent execution and serves as a design aid in service of building enduring capability for completion engineers.\u0000 This agile project is currently the only reinforcement learning/machine learning project that interfaces with the reservoir within Wells at bp. A reward function that uses root mean square error between the brain's prediction and actual field data is used as the basis to reward or punish the brain i.e., reinforcement learning. Blind training of the brain was utilized on several deployments in the field. This led to a better understanding of the data engineering requirements to feed real time data to the virtual machine. As a result of the deployment, two brains in series are being run, one for pad fluid and one for frac fluid with proppant. By obtaining a real time calibration with pad, the cost of diagnostic tests is eliminated. More importantly, the brain can significantly shorten the learning curve for new engineers and help avoid typical execution mistakes. Finally, the vast memory capacity of an agent and its ability to compare various pressure traces in short time frames can eventually be used for predictive ability as the technology matures.\u0000 This project is acknowledged as a groundbreaking project within bp and industry. The concept can be replicated at scale to various other petroleum engineering operations given the vast amount of historical data available in various disciplines.","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121225340","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}

{"title":"Hydraulic Fracture Width Inversion Using Simultaneous Downhole Tiltmeter and Distributed Acoustic Sensing Data","authors":"P. Moradi, D. Angus, M. Sharma","doi":"10.2118/209149-ms","DOIUrl":"https://doi.org/10.2118/209149-ms","url":null,"abstract":"\u0000 A new geomechanical modeling approach is presented to estimate hydraulic fracture width based upon joint inversion of near-field and far-field slow deformation data measured, respectively, by Distributed Acoustic Sensing (DAS) and Downhole Tiltmeter Fracture Mapping (DTFM) systems. Each measurement provides a unique and different perspective of the fracture. By substantially reducing the non-uniqueness of possible interpretations, integrating simultaneous deformation measurements allows us to resolve the fracture opening with higher confidence and resolution. We provide a step-by-step modeling workflow, verify our approach using synthetic data, and quantify the uncertainty. To our knowledge, no prior studies have attempted to quantitatively integrate DTFM and low-frequency DAS to obtain estimates of fracture geometry. The resulting correlation between fracture opening and fiber strain can then be applied to other stages in the treatment well that fall outside the detectability range of DTFM and only have fiber data available.","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131126836","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}

{"title":"Efficiency and Effectiveness - A Fine Balance: An Integrated System to Improve Decisions in Real-Time Hydraulic Fracturing Operations","authors":"S. Mondal, Ashan Garusinghe, Sebastian Ziman, Muhammed Abdul-Hameed, R. Paleja, Matthew Jones, J. Limbeck, Bryce Bartmann, Jeremy S Young, Kent Shanley, Bonner Cardwell, Humphrey Klobodu, P. Huckabee, G. Ugueto, Christopher Ledet","doi":"10.2118/209127-ms","DOIUrl":"https://doi.org/10.2118/209127-ms","url":null,"abstract":"\u0000 Hydraulic fracturing is a key driver of well productivity and field development planning, in addition to being the most significant portion of capex in shales. Recent breakthroughs in connectivity and digital technologies have enabled the monitoring and analyses of frac operations in real-time. However, most of the digitalization effort to date has been focused on increasing operational efficiency to reduce cost. Without an equal consideration for creating effective fracture geometries, this may lead to poor resource recovery and leave significant value behind. In this paper, we - 1) demonstrate the need to balance between optimizing fracture efficiency and effectiveness; 2) present an integrated system for frac optimization using real-time, historical data along with organizational knowledge; and 3) discuss the challenges of setting up such a system and key considerations, along with examples of large, untapped potential that can be unlocked with data science to deliver real value.\u0000 Currently, several service providers exist to stream frac data with interactive analytics dashboards. While they offer some customizability, most do not provide a true frac optimization platform that goes beyond frac monitoring and analytics geared towards efficiency and cost indicators. We are still dependent on an individual operator's experience and rules of thumb to make job decisions during a frac stage. In this paper, a real-time optimization workflow is presented that uses advanced data science and statistical techniques to interpret and predict time-series treatment data, integrate historical and contextual information, and honor basin-specific knowledge that has been gathered and tested over the years.\u0000 Examples are presented from diagnostic pads that highlight the need for balancing stimulation effectiveness with efficiency. We demonstrate a platform to host and execute an ensemble of models and visualizations that communicate actionable insights to an operator within minutes of identifying an event, gather feedback, and learn. Results from field testing show that our system accelerates the learning curve, enables consistent decision making by operators, and can generate significant cost savings. Finally, we share learnings from our digitalization journey.\u0000 Completion and stimulation expenses account for approximately half of an unconventional well cost. Automated decision making for real-time fracture treatment is the holy grail of digital completions in shales. However, a blind pursuit of efficiency may lead to sub-par fracture treatments and significant value erosion for shale assets. We present an integrated framework that connects real-time data and organizational knowledge to guide an operator to pump the best frac stage while reacting to formation response within a set of constraints. To the best of our knowledge, this is the first paper to describe the general architecture and demonstrate the viability of such a system that relies only on standard","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133435413","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}

{"title":"CounterProp Delivering Beneficial Proppant Flowback Mitigation and Improved Well Productivity - A Success Story from Permian","authors":"James Cooper, Amit Singh, Christopher Lannen, Amos Kim, Mauro Menconi, Bryce Taylor, Xinghui Liu, Jiehao Wang","doi":"10.2118/209138-ms","DOIUrl":"https://doi.org/10.2118/209138-ms","url":null,"abstract":"\u0000 Two key goals for proppant selection and injection optimization are economical mitigation of proppant flowback and achieving optimum conductivity throughout fracture area from near wellbore to far field. Improvements in these areas benefit project economics through cost reduction and production uplift. This paper highlights the implementation learnings and success results of CounterProp to reduce proppant flowback and increase near wellbore conductivity in Permian shale and tight development.\u0000 CounterProp proppant injection sequence (large particle size proppant pumped first before small particle size) was introduced by Ely et al (2019) in the paper SPE-194370-MS for slickwater treatment in low permeability shale and tight reservoirs. Two internal studies were carried out to validate CounterProp design impacts. Hydraulic fracture simulation models were completed, and laboratory study was performed to improve understanding of proppant transport behavior and evaluate impact of CounterProp. The laboratory study used large-scale tortuous slot flow equipment with slickwater and different proppant sizes and sequences. The model and laboratory study results indicated that larger proppant settled near wellbore, creating a barrier to prevent smaller proppant grains moving into the wellbore, thereby mitigating proppant flowback and generating higher fracture conductivity in near wellbore area.\u0000 An iterative and phased approach for field implementation was applied to reduce the execution risk of injecting large size 40/70 mesh proppant before small size 100 mesh with slickwater. Multiple field trials have been executed since late 2019 in both the Midland Basin and Delaware Basin. The first trial was implemented in a Lower Spraberry development in Midland Basin where proppant flowback was not an issue, a production uplift was expected with higher near wellbore conductivity, and the perceived execution risk was minimal. In Lower Spraberry, a range of median uplift from 5-42% cumulative oil was measured compared to offset control pads. In Delaware Basin, proppant flowback during early production phase is an issue handled with surface sand separator equipment, and the goal for CounterProp was to reduce the time and cost of sand separator equipment on location. CounterProp was implemented in multiple Wolfcamp developments in Delaware Basin with no execution issues and resulted in decreased proppant production compared to similar developments thereby allowing release of proppant flowback management equipment from these locations approximately 70% earlier, contributing to savings and production benefit with higher pressure drawdown allowed.\u0000 This paper shares a success story from specific examples of CounterProp applications with slickwater in Permian low permeability shale and tight reservoirs. CounterProp field trials have proven as low risk solutions for unconventional development with large potential upside of cost reduction from proppant flowback mitigation","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"155 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128700463","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}

{"title":"An Investigation of Candidate Mechanisms for Hydraulic Fracture Swarming through High-Fidelity Numerical Modeling","authors":"Morris Joseph, P. Fu, C. Sherman, Hui Wu, Ji-xiang Huang, W. Fu","doi":"10.2118/209126-ms","DOIUrl":"https://doi.org/10.2118/209126-ms","url":null,"abstract":"\u0000 Many have attributed the success of hydraulic fracturing in unconventionals to the development of increased reservoir contact area through complex fracture networks with significant shear-enhancement of flow on preexisting natural fractures. Core samples recovered in recent field experiments have revealed large numbers of subparallel hydraulic fractures (swarms) that provide an alternative explanation for the enhanced stimulated surface area. We argue that understanding the mechanisms behind the development of this stimulated surface area is key to optimizing the stimulation of unconventional resources. We investigate potential phenomena that could lead to fracture swarming. First, we consider whether \"swarming\" is indeed a correct term. We show that the null hypothesis that the hydraulic fractures are actually randomly placed cannot be rejected with significant confidence. We next investigate several mechanical explanations for the genesis of closely-spaced fractures (branching, poromechanics, variations in situ stress orientation, and near wellbore effects associated with perforations). Our analysis eliminated all mechanisms other than near wellbore effects to be the primary cause for the observed closely spaced hydraulic fractures.","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128784941","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}

{"title":"Automated Hydraulic Fracturing Integrated with Predictive Machine Learning","authors":"E. Butler, Dryonis Pertuso, Gerald Hua, P. Stark","doi":"10.2118/209165-ms","DOIUrl":"https://doi.org/10.2118/209165-ms","url":null,"abstract":"\u0000 In unconventional wells, returns are driven in part by the reduction of variability in efficiency and performance. In 2021 the stimulation of two wells in the Bakken proceeded under the architecture of an automated frac control system communicating directly to Machine Learning predictive models at the headquarters of the well operator. This represents the first time an algorithmic frac was conducted via automation, adjusting stage designs, and pushing those stage designs to the field without human intervention.\u0000 This paper discusses a fully integrated and automated completion performed on the operator's four- well pad in the Bakken. It reviews the impact on completion performance, completion design, components of the system and execution. Throughout the completion, automated software interfaced with the frac control system executing the job. Additionally, data was uploaded live and fed to the Machine Learning predictive model. This allowed the model to learn from actual well data and suggest improvements. Improvements were captured, iterated on, and design updates were sent back to the control system for the next stage in the completion sequence. Human oversight was conducted but only as a check, during the entire process.\u0000 Both the automated frac control system and algorithmic design system were functionally separate but communicated live, allowing the operator to take advantage of their complete basin knowledge database without compromising data integrity and model confidentiality.\u0000 Additionally, sensors provided real-time data such as treating pressure, rate and proppant concentration, as well as downhole data such as cluster uniformity, fracture geometry, and offset well interactions.\u0000 The project was launched with several primary goals in mind: First was to functionally test the automation of the frac fleet for the operator proving its ability to consistently place their designs.Second was to incorporate the prediction model algorithms into completion design and test how quickly and how much the Machine Learning models could actually learn from actual well stages.\u0000 Both of these primary goals were achieved, validating the ability to automatically execute completions and to tie design changes live to a control system elsewhere.\u0000 This represents the first time a hydraulic fracture was conducted via automation with algorithmic integrated design improvement, either independently or together. These capabilities can improve execution and performance where it is becoming increasingly difficult to deliver step changes in well performance with current manual crews and technology. Integrated automation provides an upgrade to completion performance by reducing variability in execution and well performance while also enabling tailored designs on scales previously unattainable.","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114813818","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}

{"title":"A Computational Model to Simulate Proppant Transport and Placement in Rough Fractures","authors":"Facheng Gong, Hai Huang, T. Babadagli, H. Li","doi":"10.2118/209150-ms","DOIUrl":"https://doi.org/10.2118/209150-ms","url":null,"abstract":"\u0000 Hydraulic fracturing creates rough fracture surfaces, instead of smooth ones, in subsurface formations. It is challenging to simulate the complex proppant transport phenomena in rough fractures due to the roughness effect as well as the complex nature of the coupled particle-fluid two-phase flow. This study first establishes realistic rough fracture models using digital scanning images of real fracture surfaces, and then conducts numerical simulations on the proppant transport and placement dynamics occurring on those rough surfaces. The digital scanning images of the artificially created tensile fractures are used to establish the geometry models of the rough fractures. The Computational Fluid Dynamic (CFD) method is adopted to describe the fluid flow, while the Discrete Element Method (DEM) is adopted to describe the particle motion. A resolved CFD-DEM coupling approach is established to simulate the fluid-granular interactions by properly modeling the momentum exchange between fluid flow and particle motion. We obtain the following preliminary simulation results: the proppant transport and settlement characteristics in rough fractures appear to be drastically different from those in smooth fractures, and the roughness feature tends to increase particle-wall and particle-particle contact. We observe an attenuated particle velocity in rough fractures compared to what occurs in smooth fractures. Additionally, the roughness increases the possibility of proppant settling when particles encounter a location with a large roughness height. Through comparison of the proppant transport phenomena in flat and rough fractures, it is observed that there is a great chance for the rough fractures to create tree-like proppant dunes, which would be beneficial for forming a proppant-filled flow channel with a higher and more sustainable conductivity.","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127170109","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}

{"title":"Time-Lapse Monitoring of Inter-Well Communication and Drainage Frac Height Using Geochemical Fingerprinting Technology with Case Studies in the Midland Basin","authors":"S. Ge, Malcolm Kintzing, M. Jin, Jiang Wu, Jana Bachleda, Faye Liu","doi":"10.2118/209145-ms","DOIUrl":"https://doi.org/10.2118/209145-ms","url":null,"abstract":"\u0000 In this study, we present a method to monitor inter-well communication and drainage frac height through time based on unique geochemical fingerprint data collected from oils. This method does not require expensive instruments or interfere with production and its results were integrated with independent pressure gauge and well performance data to enable data-driven decisions on well spacing, well stacking and completion designs with different subsurface configurations.\u0000 Produced oils of 9 horizontal wells over ~20 months and cuttings samples from the vertical section of an adjacent well were collected in the study area located in Midland Basin, Texas. The 9 producing wells were in two DSUs with two different well configurations, one drilled with two landing targets while the other with three. Thousands of chemical compounds that are naturally occurring in the produced oils and oils extracted from the cuttings, were profiled and interpreted geochemically. Drainage frac heights and quantitative production allocation by zone were conducted by building a geochemistry-based model correlating the produced oils back to their contributing intervals represented by the cuttings samples and their vertical depths. The probability of inter-well fluid communication between each of the well pairs was calculated based on the similarity of the geochemical fingerprints in the produced oils of the corresponding wells.\u0000 Our data showed that the three targets scenario generated significantly more overlapped drainage vertically and vertical well communication than the two targets scenario. Up to ~60% of the drainage frac height of the Middle well in the three targets scenario overlapped with the Upper wells and ~20% overlapped with the Lower wells, while in the two targets scenario the Upper and Lower wells only showed ~40% overlapped vertical drainage. The lateral geochemical similarity index (SIL) calculation showed correlation between higher SIL (stronger lateral well communication) and poorer oil production rates, indicating well communication could impair well performance. The data also showed significant variation of lateral communication through time which was strongest about a month into production and then reduced through time. Even with the same spacing and completion design, the Upper wells showed higher SIL (i.e., more communication) than the Lower wells, indicating geology and completion design parameters such as sand and fluid volume, and clusters should be taken into consideration for future planning. Independent pressure data also supported these observations, providing critical evidence for optimizing the stacking, spacing, and completion designs of future development wells.\u0000 Geochemical data in the produced oils carry significant information to reveal subsurface fluid flow and well interaction through time. It provides actionable data to support various field development decisions such as well spacing, well stacking, landing target optimization, wel","PeriodicalId":224883,"journal":{"name":"Day 2 Wed, February 02, 2022","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123409801","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}