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ABSTRACT The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development
This study presents the application of a data-driven workflow for evaluating the completion design and production performance of the horizontal Wolfcamp wells located in the Midland Basin at the Hydraulic Fracturing Test Site (HFTS1). Leveraging the diverse and comprehensive datasets available at HFTS, the impact of various factors including completion design, reservoir properties, well spacing, and geospatial distribution of more than 400 hydraulic fracturing stages on the well performance is evaluated.
The proposed workflow assesses the impact of variations in the reservoir properties and completion design parameters on the formation response to the hydraulic fracturing work as well as production performance. It exhibits that the fracturing gradients calculated based on the measured instantaneous shut-in pressures (ISIP) are good indicators of the formation heterogeneity along the laterals in both the upper and middle Wolfcamp formations. Fracturing gradients are strongly correlated with both reservoir properties and well treatment factors and production performances are highly impacted by the inter-well communications resulted from the fracturing behavior.
The supervised multivariate analysis in this work provides an insight into the importance of selecting the optimum completion design on a well by well basis, highlighting the importance of adapting the design of hydraulic fracturing stages to the formation characteristics along the lateral placements of the horizontal wells by adjusting the perforation densities and proppant load. It also indicates that the presence of the offset verticals contributes to the fracture network complexity which positively impacts the ultimate fracturing potential in the nearby stages. Results suggest that aggressive stimulation in the regions with a higher range of fracturing gradient and higher clay content adversely impacted the production performance. It is also observed that the best performing wells, from the oil production standpoint, are those that experienced completion and treatment variations compatible with the formation characteristics along the laterals and improved fracturing techniques.
Four main categories of data are used in this workflow including formation parameters, completion design attributes, geospatial distribution of hydraulic fracturing stages, and the formation response to the hydraulic fracturing work. This workflow utilizes data from different disciplines to explain how different parameters can impact the production behavior of a well.
Tran, Ngoc Lam (University of Oklahoma) | Gupta, Ishank (University of Oklahoma) | Devegowda, Deepak (University of Oklahoma) | Jayaram, Vikram (Pioneer Natural Resources) | Karami, Hamidreza (University of Oklahoma) | Rai, Chandra (University of Oklahoma) | Sondergeld, Carl H. (University of Oklahoma)
Summary In this study, we demonstrate the application of an interpretable (or explainable) machine‐learning workflow using surface drilling data to identify fracturable, brittle, and productive rock intervals along horizontal laterals in the Marcellus Shale. The results are supported by a thorough model‐agnostic interpretation of the input/output relationships to make the model explainable to users. The methodology described here can easily be generalized to real‐time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production, and minimizing fracture hits. In practice, this information is rarely available in real time and requires tedious and time‐consuming processing of logs (including image logs), core, microseismic data, and fiber‐optic‐sensor data to provide post‐job validation of fracture and well placement. Post‐completion analyses are generally too late for corrective action, leading to wells with a low probability of success and increasing risk of fracture hits. Our workflow involves identifying geomechanical facies from core and well‐log data. We verify that the geomechanical facies derived using core and well‐log data have characteristically different brittleness, fracturability, and production characteristics. We test and investigate several different supervised classifiers to relate surface drilling data to the geomechanical facies. The data were divided into training and test data sets, with supervised classification techniques being able to accurately predict the geomechanical facies with 75% accuracy on the test data set. The clusters predicted on test well (unseen data) were qualitatively verified using the microseismic interpretation. The use of Shapley additive explanations (SHAP) helps explain the predictive models, rank the importance of various inputs in the prediction of the facies, and provides both local and global sensitivities. Our study demonstrates that pre‐existing natural‐fracture networks control both the hydraulic‐fracture geometry as well as the production. Natural fractures promote the formation of complex fracture networks with shorter half‐lengths, which increase well productivity while minimizing fracture hits and neighboring‐well interactions. The natural‐fracture network is itself controlled by the geomechanical properties of the rock. The ability of the surface drilling data to reliably predict the geomechanical rock facies provides a powerful tool for real‐time optimization of wellbore trajectory and completions.
Lam Tran, Ngoc (University of Oklahoma) | Gupta, Ishank (University of Oklahoma) | Devegowda, Deepak (University of Oklahoma) | Karami, Hamidreza (University of Oklahoma) | Rai, Chandra (University of Oklahoma) | Jayaram, Vikram (Pioneer Natural Resources) | Sondergeld, Carl (University of Oklahoma)
This study demonstrates the application of an interpretable (or explainable) machine learning workflow using surface drilling data to identify fracable, brittle and productive rock intervals along horizontal laterals in the Marcellus shale. The results are supported by a thorough model-agnostic interpretation of the input-output relationships to make the model explainable to users. The methodology described here can easily be generalized to real-time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production and minimizing frac hits. In practice, this information is rarely available in real-time and requires tedious and time-consuming processing of logs (including image logs), core, microseismic data and fiber optic sensor data to provide post-job validation of frac- and well-placement. Post-completion analyses are generally too late for corrective action leading to wells with a low probability of success and increasing risk of frac hits. Our workflow involves identifying geomechanical facies from core- and well-log data. We verify that the geomechanical facies derived using core- and well-log data have characteristically different brittleness, fracability and production characteristics. We test and investigate several different supervised classifiers to relate surface drilling data to the geomechanical facies. The data was divided into training and test datasets, with supervised classification techniques being able to accurately predict the geomechanical facies with 75% accuracy on the test dataset. The clusters predicted on test well (unseen data) were qualitatively verified using the microseismic interpretation. The use of Shapley Additive Explanations (SHAP) helps explain the predictive models, rank the importance of various inputs in the prediction of the facies and provides both local and global sensitivities. Our study demonstrates that pre-existing natural fracture networks control both the hydraulic fracture geometry as wells as the production. Natural fractures promote the formation of complex fracture networks with shorter half-lengths which increase well productivity while minimizing frac hits and neighboring well interactions. The natural fracture network is itself controlled by the geomechanical properties of the rock. The ability of the surface drilling data to reliably predict the geomechanical rock facies provides a powerful tool for real-time optimization of wellbore trajectory and completions.
Liu, Chuxi (The University of Texas at Austin) | Yu, Wei (Sim Tech LLC.) | Chang, Cheng (The University of Texas at Austin) | Li, Qiwei (Petrochina Southwest Oil&Gas Field Company) | Sepehrnoori, Kamy (The University of Texas at Austin)
A robust and reliable workflow for well spacing optimization in shale reservoirs development incorporating various types of uncertainties and detailed economics analysis is paramount to achieve a sustainable unconventional production. In this study, we show a novel well spacing optimization workflow based on the results of assisted history matching and apply it to a real shale gas well, incorporating uncertainty parameters such as matrix permeability, matrix porosity, fracture half-length, fracture height, fracture width, fracture conductivity and fracture water saturation. The input ranges of these parameters are 10 nd to 1000 nd, 0.038 to 0.083, 200 ft to 780 ft, 25 ft to 65 ft, 0.1 ft to 4 ft, 10 md-ft to 200 md-ft, and 0.5 to 0.9 respectively and are determined from field experience and exisiting information. Results from assisted history matching are gathered with a total of 60 HM (history matching) solutions out of 325 runs that meets the criteria of BHP (bottomhole pressure) error less than 25% and WGR (water gas ratio) error less than 60%. A total of 1548 proxy solutions out of 100,000 samplings are obtained from MCMC (markov chain monte carlo) algorithm. Embedded discrete fracture model (EDFM) is used to model hydraulic fractures along with a commercial reservoir simulator. The use of greatly facilitates the modelling process of hydraulic fractures compared to the LGR (local grid refinement) method. The uncertainty parameter distributions from our workflow is matching the posterior distribution obtained from assisted history matching. It is found out that the optimal well spacing is approximately 885 ft, with an estimated net present value (NPV) of 6.67 million dollars. Economic uncertainty evaluation is performed and it is discovered that the NPV distribution obtained from the history matching solution is more concave than the results obtained from KNN (k-nearest neighbor) proxy prediction. The optimal well spacing of 885 ft obtained from this workflow is matching closely with the field experience of approximately 1000 ft. The P50 of the NPV distributions of five spacing (1550 ft, 1033 ft, 775 ft, 620 ft, and 517 ft) are 4.04, 5.91, 7.35, 6.42, and 5.44 million dollars respectively. Gas estimated ultimate recovery per well (P50) for the abovementioned spacings are 3070, 3020, 2945, 2750, and 2565 million cubic feet respectively. There is a drastic drop of gas estimated ultimate recovery per well about 6.6% going from a spacing of 775 ft to 620 ft, indicating the onset of well interference between these distances’ range. The practicality and the convenience of our workflow make it possible to be applied to any shale gas well.
Unconventional reservoirs, such as Permian Basin, have fundamentally different production behaviors than that of the conventional reservoirs because of the low permeability of formations away from the stimulated volume. Thus, it is difficult to run full-field reservoir simulation to generate a full-field development plan. Even though satisfactory history matching for completion and production data can be achieved for wells at one location, it is difficult to directly apply the results to other areas in the same region. This is especially true in complex thick pay zone reservoirs, such as Permian Basin, where the complex geology, geomechanical properties, and resource properties all make the solution of frac hit (optimal well spacing) very challenging. To our best knowledge, this study is the first to integrate the detailed physics-based simulation, including fracturing simulation, coupled reservoir and geomechanics simulation, with machine learning (ML) to generate a sound workflow for well spacing optimization. In this workflow, a large field is first divided into several representative regions according to geology, geomechanical properties, and reservoir properties; and a typical well is selected for each region. High-quality physics simulation (including fracturing simulation, coupled reservoir simulation and geomechanics simulation) and history matching are then performed for a pair of parent and child wells. Additional well completion scenarios are built upon the base case, which serve as input to the following ML study. Various ensemble regression methods are applied to generate production predictions for unexplored reservoir locations in this field.
In recent years, many fracture simulation models have been developed to represent the complex geomechanical processes involved in hydraulic fracturing (F. Ajisafe, Shan, Alimahomed, Lati, & Ejofodomi, 2017; Morales et al., 2016; Pankaj, 2018b). Among them, well interference or well spacing optimization is a critical issue to solve in energy sector, especially during the current industrial downturn (F. O. Ajisafe, Solovyeva, Morales, Ejofodomi, & Porcu, 2017; Pankaj, 2018a; L. Wang & Yu, 2019; M. Wang, Wang, Zhou, & Yu, 2019). Detailed mechanistic study of integrated fracturing simulation and reservoir simulation have made significant progress during the recent years, which greatly helps to unlock unconventional resources and assist the industry to achieve economic goals (Alimahomed et al., 2017; Lashgari, Sun, Zhang, Pope, & Lake, 2019; Min, Sen, Ji, & Sullivan, 2018; Rodriguez, 2019). Nevertheless, when applying these technologies in unconventional field development and production, major uncertainties remain, including geology aspects such as stress orientation, stress anisotropy and natural fracture distribution, and completion aspects such as discrepancy between different completion strategies (Pankaj, Shukla, Kavousi, & Carr, 2018; Xiong, Liu, Feng, Liu, & Yue, 2019). A common concern is that even though current physics-based modeling and simulation could match the completion and historical production of a single well or multiple wells, it is still difficult to successfully transfer or scale up one small-area’s knowledge and experience to another area, because of the complexity and uncertainty of unconventional reservoirs (Xiong, Ramanathan, & Nguyen, 2019; Yeh et al., 2018).
In unconventional plays, operators commonly use scaling factors to adjust actual or expected production between groups of wells with different completions designs-e.g., increasing proppant loading from 1500 lbs/ft to 1750 lbs/ft should see 6% production uplift. Though these scalars are easily deployed, they suffer inaccuracy in the time domain away from the date at which the analysis was anchored. Here, we present a machine learning-based study of the Bakken-Three Forks play of the Williston Basin, showing that large completions designs have the biggest impact on production between IP days 90-180, with the impact steadily decreasing through time afterwards. This method can be used to build scaling factors for any completions or spacing parameter by using SHAP values (SHapley Additive exPlanations), which isolate the contribution of each feature on the model prediction. Proppant loading, fluid loading, and stage length all show strong variation in scalar impact through time. All three parameters show diminishing impact over the life of the well, with large designs showing approximately 55% uplift in rates over average designs at IP90 but only 30% uplift over small designs by IP720. In contrast, the relative importance of inter-well spacing and geology increases steadily through time. SHAP values offer a powerful method to extract scaling factors from a tree-based machine learning model. Because they can be incorporated into the model-building pipeline, they remove the need to run synthetic cases or build partial dependence plots. Applying time-dependent scaling factors when making well predictions or building type curves will result in a more accurate production profile, improving decision making no matter whether the operator prefers payback or rate of return economic metrics. These methods can also be used to help answer to what extent intense designs or tight spacing accelerates or improves recovery.
Engineers making decisions on unconventional development plans often rely upon scaling factors to generate type curves for a range of potential designs. This approach is often taken when implemented designs within a given area do not fully sample the possible design choices, such as in a step-out area where none of the first-generation wells were completed with large proppant volumes. Even in a well-developed area, an operator may have only a handful of well-studied and validated type curves, yet the unique permutations of reasonable design choices can stretch into the hundreds or thousands (e.g., five potential proppant per foot values, three potential fluid per foot values, three stage lengths, five inter-well spacing choices and three lateral lengths results in 675 possible combinations). Scaling factors bridge this gap by starting from the well-understood base production expectation (e.g., a type curve, EUR estimate, or point cumulative production value) and scaling the production up or down by multiplying by a scalar, commonly derived from descriptive statistics of grouped wells (Curtis and Montalbano 2017, Srinivasan et al. 2018, Al-Alwani et al. 2019)
Continuous improvement of the completion design in horizontal wells is the key to improve the ultimate recovery from shale resources. Accounting for not only the geological characteristics of the target formation but also the spatial heterogeneity in the target layer is a significant step in achieving the optimum completion design and improving the production efficiency. For this purpose, the present study proposes a comprehensive descriptive data analytics workflow using the completion design and reservoir metrics of more than 400 fracturing stages from the eleven horizontal Wolfcamp wells in the Permian Basin at the hydraulic fracturing test site (HFTS).
In this study, fracture gradient, calculated based on the measured instantaneous shut-in pressure (ISIP), is utilized as the reservoir response to the hydraulic fracturing work. The proposed workflow evaluates the impact of variations in the reservoir properties and completion design parameters on the reservoir response to the hydraulic fracturing process. It also facilitates explaining the variations in the production performance of the horizontal wells placed in the same formation. The impact of added fracture complexity in the presence of active or inactive vertical producers located within a certain distance from the horizontal wells is also evaluated. A supervised multivariate analysis is used in this work to provide an insight into the importance of selecting the optimum completion design on a well by well basis, highlighting the importance of adapting the design of fracturing stages to the variations of the formation properties along the lateral placements of horizontal wells.
Results indicate that the best performing wells, from the cumulative oil production standpoint, are those that experienced changes in the stage completion and treatment parameters compatible with the inverted reservoir properties variations. It is also observed that in the upper Wolfcamp, formation properties dominantly control the zonal fracture gradients while in the middle Wolfcamp, completion design parameters are the dominant controllers. This workflow is used for the first time to explain the possible causes of variations in the production performance of the similarly designed HFTS wells in the Wolfcamp formation.
Rezaei, Ali (University of Houston) | Siddiqui, Fahd (University of Houston) | Dindoruk, Birol (University of Houston / Shell International Exploration and Production Inc.) | Soliman, M. Y. (University of Houston)
In order to maximize the profitability of a well and minimize the cost, three key questions must be answered before drilling a well: Where to drill the well? What completion design is to be used? Which fluid type will be produced from the reservoir? These questions must be answered under the premise of maximizing profitability. In this study, we combine the recently developed artificial neural network (ANN) model with a global sensitivity analysis method to present a reduced-order model for addressing these questions.
We developed ANN models to predict the oil and gas production of the first year. The input of the model are parameters such as longitude, latitude, true vertical depth, lateral length, fracturing fluid volume, proppant volume, and fracture stages. Next, we use the Sobol global sensitivity analysis to identify the dominant input variables and their interactions on the variation of the oil and gas production. Finally, we develop reduced-order models that can be represented as a simple algebraic expression consisting of simple mathematical functions. These equations can then be used to predict the production in the Eagle Ford shale rapidly by engineers on the field.
The ANN model used in this study predicted the oil and gas production of the first year with reasonable accuracy. Our model suggests increasing the number of fracture stages and proppant volume in the oil-bearing region. The suggestions for the gas bearing cases were opposite to the oil case. The Sobol global sensitivity approach used in this study captures the variation of the output parameters of the ANN model with respect to the changes in the input parameters. Also, it identifies the combined output variation due to the changes of multiple input parameters. After ranking the dominant contributing input parameters, the model was used to present a simple function to predict the oil and gas production of the first year (combined oil and gas). The function has the advantage to be used in a simple excel sheet and can rapidly predict the results. We compared the accuracy of the proposed reduced order model against the developed ANN model, and results showed less than 5% error in predictions.
For the first time, we have combined the data science methods with analysis of variance (ANOVA) based methods. This has resulted in a simple mathematical function to rapidly and directly predict the oil and gas from Eagle Ford shale, based on the input parameters that can be selected before drilling the well. Using the presented methodology, other such functions can be created for other shale plays and will aid engineers and decision-makers for field development to make reliable and quick decisions.
This study uses a machine learning framework to systematically analyze field production and completion data to understand the impact of frac-hits on parent and child wells and predict well spacing and completions design. Frac hits are one of the most pressing reservoir management issue that can enhance or compromise production over either the short-term or have sustained impacts over longer times. The extent of the impact is dictated by a complex interplay of petrophysical properties (high-perm streaks, mineralogy, etc.), geomechanical properties (near-field and far-field stresses, brittleness, etc.), completion parameters (stage length, cluster spacing, pumping rate, fluid and proppant amount, etc.) and development decisions (well spacing, well scheduling, etc.). As a result, the impact of frac-hits is not straightforward and difficult to predict.
The study uses data from the Meramec, Woodford and Wolfcamp formations. We develop an automated machine-learning based frac-hit detection algorithm that also quantifies the impact on the parent and child wells using matched decline curve models. We analyze about 500 parent and over 1100 child wells in the three formations. Our results show that the key factors governing the extent of the impact are the extent of depletion and producing oil rate of the parent well before frac hit, completion design parameters (fluid and proppant amount) and well spacing. Our machine learning analysis generates regression models to predict the impact of frac hits. These regression models are coupled with economic analysis to determine optimum spacing for any given completion design or optimum completion design for any given spacing.
The parent wells in all three formations had both positive and negative impact of the frac hits. Around 60–67% parent wells were negatively impacted while 33–40% wells were positively impacted. For the child wells, 71–85% wells were negatively impacted and 15–29% of the wells were positively impacted. Combining the impact on parent and child wells, the impact is dominated by the child wells as 69 to 82% of the parent-child pairs were negatively impacted and only 18–31% of the pairs were positively impacted. Considering percent loss in cumulative oil volumes in the next 5-years, in the Meramec, parent wells on average show a 16% reduction while child wells show a 39% reduction due to frac hits. The corresponding numbers for the Woodford formation are 19% and 37% and Wolfcamp formation are 20% and 22%, respectively. This translates to a parent well losing on average 40–50 thousand bbls in next five years and a child well losing on average 130–150 thousand bbls in the same period.
This study systematically analyzes available data to understand the impact of frac hits and formulates a machine learning-based well spacing-well completions matrix workflow that can easily be extended to other formations by integrating commonly available production and completions data.