Day 2 Thu, September 06, 2018最新文献

{"title":"Augmenting Hybrid Physics-Based Multivariate Analysis with the Alternating Conditional Expectations Approach to Optimize Permian Basin Well Performance","authors":"E. Lolon, Karn Agarwal, M. Mayerhofer, O. Oduba, H. Melcher, L. Weijers","doi":"10.2118/191798-MS","DOIUrl":"https://doi.org/10.2118/191798-MS","url":null,"abstract":"\u0000 The oil and gas industry has used multivariate analysis (MVA) to evaluate how geology, reservoir, and drilling/completion parameters (well characteristics) relate to well production. Although many techniques are used, multiple linear regression (MLR) has been especially popular due to its ease of use and the interpretability of its parameters. However, when the relationship between response and predictor variables is highly complex or nonlinear, this technique can yield erroneous and misleading results. Recent work showed the benefit of combining statistical MLR with fracture and numerical reservoir (physics-based) modeling, which yields a more physically realistic production response to suggested completion changes (Mayerhofer, 2017). However, this work is limited to using the nonlinear relationships between production outcomes and only a few independent variables (i.e., proppant/fluid volumes pumped and fracture spacing). For practicality, other important predictors are still assumed to be linearly correlated to the response variable (e.g., predicted cumulative oil).\u0000 In this paper, we describe the implementation of the Alternating Conditional Expectations (ACE) approach and the interpretation of its results, and we highlight the main advantages and limitations of the approach in MVA using a simulated dataset and field data from the Permian Basin. The ACE approach is a non-parametric regression method (i.e., no explicit assumption about the relationships between dependent or response and independent or predictor variables is required). It maximizes the linear correlation between the response and predictor variables in the transformed space (the optimal transformations are derived solely from the given data) resulting in higher R-squared (R2) and smaller Root-Mean-Square-Error (RMSE) values compared to those obtained from the MLR. Because a priori assumptions about the functional form for a transformation (e.g., linear, monotonic, periodic, and polynomial) do not have to be imposed, the ACE-guided transformation can give new insights into the relationship between the response and predictor variables.\u0000 We have successfully identified nonlinear relationships between well production and completion/reservoir properties for horizontal wells in the Permian Basin by means of ACE plots, and we have developed closed functional forms for these relationships. When integrated into the overall workflow of MVA, coupled with completion cost models, the ACE model could produce more realistic and accurate well performance predictions— using not only completion/reservoir parameters that are easily calibrated or \"history-matched\" as in the physics-based models, but also parameters that cannot be conveniently evaluated with the physics-based models.","PeriodicalId":11155,"journal":{"name":"Day 2 Thu, September 06, 2018","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90854603","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":"Integrating Propellant and Shaped Charges to Improve Frac Efficiency","authors":"L. Albert, Nadir Nery, H. Prapoo, P. Dai, B. Qu, Ge Jiang, D. Kwok","doi":"10.2118/191785-MS","DOIUrl":"https://doi.org/10.2118/191785-MS","url":null,"abstract":"\u0000 Current perforating guns utilize shaped charges with high energy explosives (RDX, HMX, etc.) to shoot high velocity jets of metal in a direction that is perpendicular to the gun body (and casing), thus creating holes in casing and tunnels through cemented annulus and formation. The physics of shaped charge perforating (high velocity jet of metal penetrating casing and formation) results in a crushed zone of rock around the perforation tunnel. Rock crushing increases skin and lowers permeability in the near wellbore region, making formations more difficult to breakdown and increasing treating pressures. There have been many methods developed to overcome the perforation crushed zone and improve flow efficiency. These methods include underbalance, overbalance, propellants, charge orientation and reactive shaped charge liners.\u0000 Propellants (energetic materials with slower burn rates than explosives shaped charges) have been utilized in a number of methods to create a delayed pressure pulse to break-up the crushed zone around perforation tunnels. Propellants have been added as solid sleeves over guns, solid sticks ignited across open sets of perforations and propellant discs within gun bodies. With each of these techniques, propellant is located within the wellbore when it burns, thus the pressure pulse builds within the casing and then flows into perforation tunnels. As the pressure pulse moves into perforations, it can disrupt the tunnel crushed zone and create fractures. The net effect is lower skin and improved perforation efficiency. Unfortunately, by burning propellant within the casing, the efficiency of the pressure pulse is reduced.\u0000 To tackle the challenges above, a new method is proposed to place propellant in a molded cap that attaches to the face of individual shaped charges. This new method results in jets produced by shaped charges dragging the propellant material behind the high velocity jet as it penetrates casing, cement annulus and formation. As a result, most of the propellant burns sequentially within the perforation tunnel, thus delivering direct continuum pressure pulse to the perforating event. The propellant provides a secondary stream of energy, enlarging the perforation tunnel diameter, cleaning up the perforation tunnel and giving impetus to the shaped charge jet, resulting in deeper penetration. The result is very effective and efficient in disrupting crushed zones and creating fractures around the perforation tunnel.","PeriodicalId":11155,"journal":{"name":"Day 2 Thu, September 06, 2018","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82631207","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":"Production Induced Stress Change Impact on Infill Well Flowback Operations in Permian Basin","authors":"Wei Zheng, Lili Xu, Katharine Moncada, Tao Xu, P. Pankaj, Shekhar Sinha","doi":"10.2118/191770-ms","DOIUrl":"https://doi.org/10.2118/191770-ms","url":null,"abstract":"\u0000 As many unconventional basins are maturing, infill well drilling and completion has taken the center stage in the development phase. Most operators now realize the importance of incorporating the geomechanical changes induced by stimulation and production of the parent wells while placing infill child wells. But after drilling and completion, post-stimulation flowback is also critical in maintaining well productivity and performance. To optimize field management strategy, a comprehensive understanding of the impact of production induced geomechanical property changes on infill well performance and its safe operating window for flowback operation is important. This paper investigates a well located in Permian basin and provides insights on flowback operation strategies for infill well using fully coupled finite element model and flowback simulator to help reduce fracture damage and maximize well deliverability.\u0000 First an integrated workflow coupling fracture, reservoir and geo-mechanic models is used in this paper to systematically investigate the depletion effects. Then a flowback simulator is utilized to robustly study the safe operating window for the infill well, implementing updated formation properties from previous model. A corresponding base model is created over wells completed in the Permian basin with geo-mechanical earth model generated from logs and field data. The integrated workflow with finite element computation was applied to predict the induced stress change after stimulation and production. To better understand the influence of geomechanical property changes over infill well performance and proppant flowback, a second model is also created, but without consideration of any production induced geomechanical property changes during flowback simulation. The differences of safe operating window simulated from these two models are compared and anaylized to reveal the impacts of stress change over the infill well in this area. Guidelines for adjusting choke settings and possible completion re-design are recommended to help reduce proppant flowback and improve the overall well productivity.\u0000 Based on the numerical results from above modeling study and comparison, flowback strategy is analyzed for infill well. In normal stress environment, due to the change in stress state from production and increase in differential stress around the wellbore, the choke management and possible completion design are adjusted accordingly as to reduce proppant flowback and optimize well performance. In addition, the capability of the workflow to model pressure depletion and associated stress conditions with respect to time enables us to optimize the field development for the area in long term.\u0000 The study presents a reference to strategic planning for operators and service companies to manage their infill well flowback operation on an existing pad with improved completion efficiency and stimulated volume in Permian basin. Intensive flowback operation man","PeriodicalId":11155,"journal":{"name":"Day 2 Thu, September 06, 2018","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75735963","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":"Self-Healing Biocement and Its Potential Applications in Cementing and Sand-Consolidation Jobs: A Review Targeted at the Oil and Gas Industry","authors":"C. Noshi, J. Schubert","doi":"10.2118/191778-MS","DOIUrl":"https://doi.org/10.2118/191778-MS","url":null,"abstract":"\u0000 There are several self-healing mechanisms, both natural and artificial, applied to cementitious materials. In recent years, microbially induced calcite precipitation (MICP) technology has garnered special attention in the fields of Microbiology and Civil Engineering. The technology involves the synthesis of calcium carbonate crystals at ambient temperatures in calcium rich systems. Biocementation occurs as active microbes diffuse through the cracks and micro-pits generating calcitic deposits owing to their metabolic pathway. The calcifying bacterial cultures produce urease or carbonic anhydrase enzyme which is capable of precipitating calcium in the surrounding micro environment as CaCO3. The bacterial degradation of urea locally increases the pH and stimulates the microbial deposition of carbonate. The calcium carbonate produced binds the soil particles together, thus cementing and clogging the grains, and hence improves the strength and reduces the hydraulic conductivity of the unconsolidated sands. Moreover, these precipitated crystals can thus fill the cracks and enhance the durability of cement, mortar, and concrete. Incorporating calcifying bacteria is the essence of developing a self-healing material or \"bio-cementing\" technology as bacteria behaves as a long-lasting healing agent.\u0000 The calcifying microbes can be isolated from different sources like water springs, soil, ocean, environments with high pH values or the cement itself. The purified strains can be grown for a 24-hour period in the laboratory and then blended with the cement or other materials depending on the desired application. A cheap carbon source like glycerol/molasses is supplemented to the mixture triggering fast bacterial multiplication. It was found that after the curing time of 28 days, tensile strength, micro-crack healing capacity, and durability increased significantly. The process is as simple as mixing bacteria into a cement paste. The technique for creating a high strength cement in a permeable starting material involves combining the starting material with effective amounts of (1) a urease producing micro-organism with a high urea hydrolysis rate; (2) urea; and (3) calcium ions, under standard conditions of 0.5-50 mM urea hydrolyzed min-1. Scientists found that after injecting the bacterial cementitious solution for a period of one month, the spores of three particular bacteria where still viable. Harmless bacteria such as Bacillus genus remains dormant until water enters the cracks. In this case, formation water, or water from fracturing fluids or any source can be used to trigger the bacteria. Moreover, the process does not require oxygenation.\u0000 In this paper, self-healing approaches based on bacteria will be thoroughly reviewed. The concept of biomineralization, bioclogging, and biorepair and its applications in improving the engineering properties of sands and cement is tackled. Based on the aforementioned aspects of self-healing in cementitious materials,","PeriodicalId":11155,"journal":{"name":"Day 2 Thu, September 06, 2018","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74763281","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":"Is There Anything Called Too Much Proppant?","authors":"K. Srinivasan, F. Ajisafe, Farhan Alimahomed, M. Panjaitan, S. Makarychev-Mikhailov, Bruce Mackay","doi":"10.2118/191800-MS","DOIUrl":"https://doi.org/10.2118/191800-MS","url":null,"abstract":"\u0000 Unconventional completions in North America have seen a paradigm shift in volumes of proppant pumped since 2014. There is a clear noticeable trend in both oil prices and proppant volumes – thanks to low product and service costs that accompanied the oil price crash in early 2015. As the industry continues to recover, operators are reevaluating completion designs to understand if these proppant volumes are beyond what is optimal. This paper analyzes trends in completion sizes and types across all major unconventional oil and gas plays in the US since 2011 and tracks their impact on well productivity.\u0000 Completion and production data since 2011 from more than 70,000 horizontal wells in seven major basins (Gulf Coast, Permian, Appalachian, Anadarko, Haynesville, Williston and Denver Julesburg basins) and 11 major oil/gas producing formations were analyzed to examine developments in proppant and fluid volumes. Average concentration of proppant per gallon of fluid pumped was used to understand transitional trends in fracturing fluid types with time. Production performance indicators such as First month, Best 3 or Best 12 months of oil and gas production were mapped against completion volumes to evaluate if there are added economic advantages to pumping larger designs.\u0000 In general, all major basins have seen progressive improvements in average well performance since 2011, with the Permian Basin showing the highest improvement, increasing from an average first-six-months oil production of 25,000 bbl in 2011 to 75,000 bbl in 2017. The Gulf Coast basin, where the Eagle Ford formation is located, has seen a 6-fold increase in proppant volumes pumped per foot of lateral since 2011 while the Permian and Appalachian basins hit peak proppant volumes in 2015 and 2016 respectively. In Permian and Eagleford wells, higher proppant volumes in general have resulted in better production up to a certain concentration. In Williston and Denver basins, most operators are moving away from gelled fluids, and reduced average proppant concentration per fluid volume pumped shows inclination toward hybrid or slickwater designs. While some of these observations are tied to reservoir quality, proppant volumes have begun to peak as operators have either reached an optimal point or are in the process of reducing volumes.\u0000 Demand for proppant is expected to nearly double by 2020. As oil prices continue to recover, well AFEs continue to increase, despite multiple efforts to improve capital efficiency. The need for enhanced fracture conductivity and extended half-lengths on EURs are been discussed by combining actual observed production data and sensitivities using calibrated production models. The industry is moving toward large-volume slickwater fracturing operations using smaller proppants, but he operating landscape is expected to see a correction when such designs become less economical.","PeriodicalId":11155,"journal":{"name":"Day 2 Thu, September 06, 2018","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76895376","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":"Characterization of Fracture-Driven Interference and the Application of Machine Learning to Improve Operational Efficiency","authors":"R. Klenner, Guoxiang Liu, Hayley Stephenson, Glen Murrell, N. Iyer, Nurali Virani, Anveshi Charuvaka","doi":"10.2118/191789-MS","DOIUrl":"https://doi.org/10.2118/191789-MS","url":null,"abstract":"\u0000 Frac hits are a form of fracture-driven interference (FDI) that occur when newly drilled wells communicate with existing wells during completion, and which may negatively or positively affect production. An analytics and machine-learning approach is presented to characterize and aid understanding of the root causes of frac hits. The approach was applied to a field data set and indicated that frac hits can be quantitatively attributed to operational or subsurface parameters such as spacing or depletion. The novel approach analyzed a 10-well pad comprising two ‘parent’ producers and eight ‘child’ infills. The analysis included the following data types: microseismic, completion, surface and bottomhole pressure, tracers, production, and petrophysical logs. The method followed a three-step process: 1) use analytics to assess interference during the hydraulic fracturing and during production, 2) catalogue or extract feature engineering attributes for each stage (offset distance, petrophysics, completion, and depletion) and 3) apply machine-learning techniques to identify which attributes (operations or subsurface) are significant in the causation and/or enhancement of inter-well communication. Information fusion with multi-modal data was also used to determine the probability of well-to-well communication. The data fusion technique integrated multiple sensor data to obtain a lower detection error probability and a higher reliability by using data from multiple sources. The results showed that the infill wells completed in closest proximity to the depleted parents exhibit strong communication. The machine-learning classification creates rules that enable better understanding of control variables to improve operational efficiency. Furthermore, the methodology lends a framework that enables the development of visualization, continuous learning, and real-time application to mitigate communication during completions.","PeriodicalId":11155,"journal":{"name":"Day 2 Thu, September 06, 2018","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89234258","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}