The effectiveness of secondary and tertiary recovery projects depends heavily on the operator's understanding of the fluid flow characteristics within the reservoir. 3D geo-cellular models and finite element/difference-based simulators may be used to investigate reservoir dynamics, but the approach generally entails a computationally expensive and time-consuming workflow. This paper presents a workflow that integrates rapid analytical method and data-analytics technique to quickly analyze fluid flow and reservoir characteristics for producing near "real-time" results. This fast-track workflow guides reservoir operations including injection fluid allocation, well performance monitoring, surveillance, and optimization, and delivers solutions to the operator using a website application on a cloud-based environment. This web-based system employs a continuity governing equation (Capacitance Resistance Modelling, CRM) to analyze inter-well communication using only injection and production data. The analytic initially matches production history to determine a potential time response between injectors and producers, and simultaneously calculates the connectivity between each pair of wells. Based on the inter-well relationships described by the connectivity network, the workflow facilitates what-if scenarios. This workflow is suitable to study the impact of different injection plans, constraints, and events on production estimation, performance monitoring, anomaly alerts, flood breakthrough, injection fluid supply, and equipment constraints. The system also allows automatic injection re-design based on different number of injection wells to guide injection allocation and drainage volume management for flood optimization solutions. A field located in the Midland basin was analyzed to optimize flood recovery efficiency and apply surveillance assistance. The unit consists of 11 injectors and 22 producers. After optimization, a solution delivering a 30% incremental oil production over an 18-month period was derived. The analysis also predicted several instances of early water breakthrough and high water cut, and subsequent mitigation options. This system couples established waterflood analytics, CRM and modern data-analytics, with a web-based deliverable to provide operators with near "real-time" surveillance and operational optimizations.
Liu, Guoxiang (Baker Hughes a GE Company) | Stephenson, Hayley (Baker Hughes a GE Company) | Shahkarami, Alireza (Baker Hughes a GE Company) | Murrell, Glen (Baker Hughes a GE Company) | Klenner, Robert (Energy & Environmental Research Center, University of North Dakota) | Iyer, Naresh (GE Global Research) | Barr, Brian (GE Global Research) | Virani, Nurali (GE Global Research)
Optimization problems, such as optimal well-spacing or completion design, can be resolved rapidly via surrogate proxy models, and these models can be built using either data-based or physics-based methods. Each approach has its strengths and weaknesses with respect to management of uncertainty, data quality or validation. This paper explores how data- and physics-based proxy models can be used together to create a workflow that combines the strengths of each approach and delivers an improved representation of the overall system. This paper presents use cases that display reduced simulation computational costs and/or reduced uncertainty in the outcomes of the models. A Bayesian calibration technique is used to improve predictability by combining numerical simulations with data regressions. Discrepancies between observations and surrogate outcomes are then observed to calibrate the model and improve the prediction quality and further reduce uncertainty. Furthermore, Gaussian Process Regression is used to locate global minima/maxima, with a minimal number of samples. To demonstrate the methodology, a reservoir model involving two wells in a drill space unit (DSU) in the Bakken Formation was constructed using publicly available data. This reservoir model was tuned by history matching the production data for the two wells. A data-based regression model was constructed based on machine learning technologies using the same dataset. Both models were coupled in a system to build a hybrid model to test the proposed process of data and physics coupling for completion optimization and uncertainty reduction. Subsequently, Gaussian Process Model was used to explore optimization scenarios outside of the data region of confidence and to exploit the hybrid model to further reduce uncertainty and prediction. Overall, both the computation time to identify optimal completion scenarios and uncertainty were reduced. This technique creates a robust framework to improve operational efficiency and drive completion optimization in an optimal timeframe. The hybrid modeling workflow has also been piloted in other applications such as completion design, well placement and optimization, parent-child well interference analysis, and well performance analysis.
Klenner, Rob (Baker Hughes a GE Company) | Liu, Guoxiang (Baker Hughes a GE Company) | Stephenson, Hayley (Baker Hughes a GE Company) | Murrell, Glen (Baker Hughes a GE Company) | Iyer, Naresh (GE Global Research) | Virani, Nurali (GE Global Research) | Charuvaka, Anveshi (GE Global Research)
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.
Given limited CO2 supply, operational constraints, and pattern specific reservoir performance, WAG schedule can be customized such that NPV or other metrics are optimized. Depending on the WAG schedule, recovery can fluctuate between 5–15% at the pattern scale due to reservoir heterogeneity causing variations in sweep efficiency. An analytical method was developed to optimize WAG schedules that couples traditional reservoir modeling and simulation with machine learning, enabling the discovery of optimal WAG schedules that increase recovery at the pattern level. A history-matched reservoir model of Chaparral Energy's Farnsworth Field, Ochiltree County, TX was sampled intelligently to perform predictive reservoir flow simulations and artificially build an intelligent reservoir model that samples a broad range of possible WAG scenarios for optimization. The intelligent model generates the next "best" sample to investigate in the numerical simulator and converges on the optima, quickly reducing the number of runs investigated. Results in this paper demonstrate that there can be significant improvements in net present value as well as net utilization rates of CO2 using this analytical technique. The WAG design generated by the intelligent reservoir model should be deployed in the field in early 2016 for validation. It is intended that the intelligent reservoir model will be updated on a regular basis as injection and production data is obtained. This effort represents the beginning of a paradigm shift in the application of modeling and simulation tools for significant improvements in field production operations.
This paper presents results of a screening procedure for chemical EOR methods based on fuzzy-logic and data clustering algorithms. EOR processes considered included combinations of polymer, alkali and surfactant. Reservoir parameters are represented as triangular distributions with validity limits for each EOR process, and a most likely validity value of the distribution. Another triangular distribution was used as a reference for each of the reservoir and fluid parameters. The limits of validity were defined by mining a database and also by using technical limits, e.g. maximum stability temperature for polymers. The most relevant variables were dictated by availability of data and by comparing screening results with reported field cases. Wyoming basins have a long tradition of oil and gas exploitation, so many of the assets are at an advanced stage of maturity. The current energy market has revitalized the opportunities for further exploitation of numerous reservoirs in Wyoming. Enhanced-Oil Recovery (EOR) represents an attractive target for increasing the recovery factor in many of currently underexploited reservoirs, particularly by CO2 and Chemical Flooding. Associated decision-making workflows demand screening procedures, simulation and detailed economic evaluations. Sensible screening procedures are necessary to guide decision-making exercises. In practice, it was not possible to generalize success in field cases. Our results show this simple screening procedure requires a relatively small dataset for each asset, which contains bounds and likely values for the relevant rock and fluid properties. This has been implemented in a way that facilitates its use by final users. Many of the Wyoming reservoirs represent good candidates for processes such as alkaline-polymer or straight-polymer flooding, based on published field experience and our results. This EOR screening strategy is viewed as a significant improvement over go-no go criteria based on look-up table methods, because the developed method yields indicators ranking candidates for chemical EOR strategies. This provides a more assertive search for EOR candidates and allows reservoir types to be grouped on the basis of suitability. A similar philosophy will be developed for screening of CO2 projects, providing a further step in the decision making process and risk management associated with CO2-based EOR projects.
Enhanced-Oil Recovery (EOR) presents a timely opportunity to access new oil reserves in known traps as a result of the current energy market. In this work, EOR refers to any changes in a reservoir development plan that leads to increased recovery factor by the injection of fluids/energy into the reservoir. This is much in line with the idea of tertiary recovery, which typically follows conventional waterflooding or gas injection, but that can be applied at any stage of production. Well stimulation or conformance activities, well architectures and changes in the production infrastructure, e.g. pumps or completions, are not included in the analysis of this work.
Wyoming represents a mature oil province with many of the oil reservoirs at an advanced stage of maturity (Gharbi, 2001) or in significant decline. Waterflooding has been frequently used in in Wyoming basins with varied degree of success (Towler and Griffith, 1999), so "engineered?? waterflooding (chemical flooding) can naturally follow in the many of Wyoming's reservoirs. Numerous pilot and full-field EOR projects have been completed in Wyoming, particularly polymer floods (Hochanadel et al, 1990) since the early 1980s. Micellar/Polymer flooding was developed and tested in Wyoming reservoirs, including the famous Big Muddy low-tension flood (Borah and Gregory, 1988; Ferrell et al., 1988; Gilliland and Conley, 1976). Many of the early projects resulted in technical success, but were uneconomical.