Using Geomechanical Modeling to Quantify the Impact of Natural Fractures on Well Performance and Microseismicity: Application to the Wolfcamp, Permian Basin

2015 ◽  
Author(s):  
A. Ouenes ◽  
N. Umholtz ◽  
Y. Aimene
Keyword(s):  
Natural Fractures ◽  
Well Performance ◽  
Permian Basin ◽  
The Impact

Using geomechanical modeling to quantify the impact of natural fractures on well performance and microseismicity: Application to the Wolfcamp, Permian Basin, Reagan County, Texas

2016 ◽  
Vol 4 (2) ◽  
pp. SE1-SE15 ◽  
Author(s):  
Ahmed Ouenes ◽  
Nicholas M. Umholtz ◽  
Yamina E. Aimene
Keyword(s):  
Empirical Relationship ◽  
Production Performance ◽  
Fault System ◽  
Fracture Density ◽  
Natural Fractures ◽  
Well Performance ◽  
Optimal Position ◽  
Complex Fault ◽  
Quantitative Impact ◽  
The Impact

We have evaluated workflows to quantify the mechanical impact of natural fractures (NFs) on the production performance of hydraulically stimulated stages in shale wells. Variations in fracture orientation and density can enhance or degrade the transport and effectiveness of fracturing fluids. Specifically, we studied the effect of a complex fault splay system on a horizontal Wolfcamp B reservoir well. A general workflow that combines geophysics, geology, and geomechanics (3G) was evaluated and applied to the well. The benefits of the 3G workflow are threefold. First, the quantitative impact of the NFs on the regional stress is provided through the differential horizontal stress variation, which impacts fracturing complexity. Then, the reservoir strain map, validated with microseismic data, gives insights into the stimulated drainage pathways. Finally, the ability of the [Formula: see text] integral to predict poor hydraulic fracturing stages as a function of fracture density along the wellbore or as a function of the energy required to propagate a fracture. Building on the validated 3G workflow, a well placement workflow that takes into account the quantitative impact of NFs on well performance was developed on the sample Wolfcamp well. By comparing the [Formula: see text] integral of the same completion stage in simulations with and without NFs, stages with similar [Formula: see text] integral values in both simulations were identified as those not being affected by the NF network. This allows the workflow to provide the optimal position of a well in the presence of NFs associated with a complex fault system that may produce undesirable water. The result is a validated 3G workflow that provides a geomechanical explanation for an empirical relationship showing that high oil production is achieved within a “Goldilocks” range of natural fracturing.

scholarly journals The impact of natural fractures on heat extraction from tight Triassic sandstones in the West Netherlands Basin: a case study combining well, seismic and numerical data

2021 ◽  
Vol 100 ◽  
Author(s):  
Quinten D. Boersma ◽  
Pierre Olivier Bruna ◽  
Stephan de Hoop ◽  
Francesco Vinci ◽  
Ali Moradi Tehrani ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Heat Production ◽  
Positive Impact ◽  
Heat Extraction ◽  
Natural Fractures ◽  
Negative Power ◽  
Petrophysical Analysis ◽  
Production Values ◽  
Geostatistical Inversion ◽  
The Impact

Abstract The positive impact that natural fractures can have on geothermal heat production from low-permeability reservoirs has become increasingly recognised and proven by subsurface case studies. In this study, we assess the potential impact of natural fractures on heat extraction from the tight Lower Buntsandstein Subgroup targeted by the recently drilled NLW-GT-01 well (West Netherlands Basin (WNB)). We integrate: (1) reservoir property characterisation using petrophysical analysis and geostatistical inversion, (2) image-log and core interpretation, (3) large-scale seismic fault extraction and characterisation, (4) Discrete Fracture Network (DFN) modelling and permeability upscaling, and (5) fluid-flow and temperature modelling. First, the results of the petrophysical analysis and geostatistical inversion indicate that the Volpriehausen has almost no intrinsic porosity or permeability in the rock volume surrounding the NLW-GT-01 well. The Detfurth and Hardegsen sandstones show better reservoir properties. Second, the image-log interpretation shows predominately NW–SE-orientated fractures, which are hydraulically conductive and show log-normal and negative-power-law behaviour for their length and aperture, respectively. Third, the faults extracted from the seismic data have four different orientations: NW–SE, N–S, NE–SW and E–W, with faults in proximity to the NLW-GT-01 having a similar strike to the observed fractures. Fourth, inspection of the reservoir-scale 2D DFNs, upscaled permeability models and fluid-flow/temperature simulations indicates that these potentially open natural fractures significantly enhance the effective permeability and heat production of the normally tight reservoir volume. However, our modelling results also show that when the natural fractures are closed, production values are negligible. Furthermore, because active well tests were not performed prior to the abandonment of the Triassic formations targeted by the NLW-GT-01, no conclusive data exist on whether the observed natural fractures are connected and hydraulically conductive under subsurface conditions. Therefore, based on the presented findings and remaining uncertainties, we propose that measures which can test the potential of fracture-enhanced permeability under subsurface conditions should become standard procedure in projects targeting deep and potentially fractured geothermal reservoirs.

Data-Driven Causal Analyses of Parent-Child Well Interactions for Well Spacing Decisions

2021 ◽  
Author(s):  
Rohan Sakhardande ◽  
Deepak Devegowda
Keyword(s):  
Causal Inference ◽  
Randomized Clinical Trials ◽  
Drug Efficacy ◽  
Complex Problem ◽  
Petroleum Engineering ◽  
Well Performance ◽  
Well Spacing ◽  
The Impact ◽  
Naive Approach ◽  
Parent Child

Abstract The analyses of parent-child well performance is a complex problem depending on the interplay between timing, completion design, formation properties, direct frac-hits and well spacing. Assessing the impact of well spacing on parent or child well performance is therefore challenging. A naïve approach that is purely observational does not control for completion design or formation properties and can compromise well spacing decisions and economics and perhaps, lead to non-intuitive results. By using concepts from causal inference in randomized clinical trials, we quantify the impact of well spacing decisions on parent and child well performance. The fundamental concept behind causal inference is that causality facilitates prediction; but being able to predict does not imply causality because of association between the variables. In this study, we work with a large dataset of over 3000 wells in a large oil-bearing province in Texas. The dataset includes several covariates such as completion design (proppant/fluid volumes, frac-stages, lateral length, cluster spacing, clusters/stage and others) and formation properties (mechanical and petrophysical properties) as well as downhole location. We evaluate the impact of well spacing on 6-month and 1-year cumulative oil in four groups associated with different ranges of parent-child spacing. By assessing the statistical balance between the covariates for both parent and child well groups (controlling for completion and formation properties), we estimate the causal impact of well spacing on parent and child well performance. We compare our analyses with the routine naïve approach that gives non-intuitive results. In each of the four groups associated with different ranges of parent-child well spacing, the causal workflow quantifies the production loss associated with the parent and child well. This degradation in performance is seen to decrease with increasing well spacing and we provide an optimal well spacing value for this specific multi-bench unconventional play that has been validated in the field. The naïve analyses based on simply assessing association or correlation, on the contrary, shows increasing child well degradation for increasing well spacing, which is simply not supported by the data. The routinely applied correlative analyses between the outcome (cumulative oil) and predictors (well spacing) fails simply because it does not control for variations in completion design over the years, nor does it account for variations in the formation properties. To our knowledge, there is no other paper in petroleum engineering literature that speaks of causal inference. This is a fundamental precept in medicine to assess drug efficacy by controlling for age, sex, habits and other covariates. The same workflow can easily be generalized to assess well spacing decisions and parent-child well performance across multi-generational completion designs and spatially variant formation properties.

Machine Learning and Artificial Intelligence Provides Wolfcamp Completion Design Insight

2021 ◽  
Author(s):  
Robert Shelley ◽  
Oladapo Oduba ◽  
Howard Melcher
Keyword(s):  
Machine Learning ◽  
Water Saturation ◽  
Saturation Pressure ◽  
Ann Model ◽  
Permian Basin ◽  
Treatment Rate ◽  
Feed Forward Neural Network ◽  
Well Production ◽  
Reservoir Type ◽  
The Impact

Abstract The subject of this paper is the application of a unique machine learning approach to the evaluation of Wolfcamp B completions. A database consisting of Reservoir, Completion, Frac and Production information from 301 Multi-Fractured Horizontal Wolfcamp B Completions was assembled. These completions were from a 10-County area located in the Texas portion of the Permian Basin. Within this database there is a wide variation in completion design from many operators; lateral lengths ranging from a low of about 4,000 ft to a high of almost 15,000 ft, proppant intensities from 500 to 4,000 lb/ft and frac stage spacing from 59 to 769 ft. Two independent self-organizing data mappings (SOM) were performed; the first on completion and frac stage parameters, the second on reservoir and geology. Characteristics for wells assigned to each SOM bin were determined. These two mappings were then combined into a reservoir type vs completion type matrix. This type of approach is intended to remove systemactic errors in measuement, bias and inconsistencies in the database so that more realistic assessments about well performance can be made. Production for completion and reservoir type combinations were determined. As a final step, a feed forward neural network (ANN) model was developed from the mapped data. This model was used to estimate Wolfcamp B production and economics for completion and frac designs. In the performance of this project, it became apparent that the incorporation of reservoir data was essential to understanding the impact of completion and frac design on multi-fractured horizontal Wolfcamp B well production and economic performance. As we would expect, wells with the most permeability, higher pore pressure, effective porosity and lower water saturation have the greatest potential for hydrocarbon production. The most effective completion types have an optimum combination of proppant intensity, fluid intensity, treatment rate, frac stage spacing and perforation clustering. This paper will be of interest to anyone optimizing hydraulically fractured Wolfcamp B completion design or evaluating Permian Basin prospects. Also, of interest is the impact of reservoir and completion characteristics such as permeability, porosity, water saturation, pressure, offset well production, proppant intensity, fluid intensity, frac stage spacing and lateral length on well production and economics. The methodology used to evaluate the impact of reservoir and completion parameters for this Wolfcamp project is unique and novel. In addition, compared to other methodologies, it is low cost and fast. And though the focus of this paper is on the Wolfcamp B Formation in the Midland Basin, this approach and workflow can be applied to any formation in any Basin, provided sufficient data is available.

Evaluating Infill Well Performance and Fracture Driven Interactions Using Intervention Based Distributed Fiber Optics

2021 ◽  
Author(s):  
Ahmed Attia ◽  
Matthew Lawrence
Keyword(s):  
Fiber Optics ◽  
Fracture Network ◽  
Data Sets ◽  
Well Performance ◽  
Design Strategies ◽  
Data Set ◽  
Geological Features ◽  
Production Output ◽  
Long Carbon Fiber ◽  
The Impact

Abstract Distributed Fiber Optics (DFO) technology has been the new face for unconventional well diagnostics. This technology focuses on measuring Distributed Acoustic Sensing (DAS) and Distrusted Temperature Sensing (DTS) to give an in-depth understanding of well productivity pre and post stimulation. Many different completion design strategies, both on surface and downhole, are used to obtain the best fracture network outcome; however, with complex geological features, different fracture designs, and fracture driven interactions (FDIs) effecting nearby wells, it is difficult to grasp a full understanding on completion design performance for each well. Validating completion designs and improving on the learnings found in each data set should be the foundation in developing each field. Capturing a data set with strong evidence of what works and what doesn't, can help the operator make better engineering decisions to make more efficient wells as well as help gauge the spacing between each well. The focus of this paper will be on a few case studies in the Bakken which vividly show how infill wells greatly interfered with production output. A DFO deployed with a 0.6" OD, 23,000-foot-long carbon fiber rod to acquire DAS and DTS for post frac flow, completion, and interference evaluation. This paper will dive into the DFO measurements taken post frac to further explain what effects are seen on completion designs caused by interferences with infill wells; the learnings taken from the DFO post frac were applied to further escalate the understanding and awareness of how infill wells will preform on future pad sites. A showcase of three separate data sets from the Bakken will identify how effective DFO technology can be in evaluating and making informed decisions on future frac completions. In this paper we will also show and discuss how DFO can measure real time FDI events and what measures can be taken to lessen the impact on negative interference caused by infill wells.

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