The Power of Predictive Analytics in Oil Field Development: Integrating Machine Learning with Reservoir Hydrocarbon Data to Enable Enhanced Oil Recovery of Hugin Formation within the Theta Vest Structure

2021 ◽  
Author(s):  
L. T. Hardanto
Keyword(s):  
Machine Learning ◽  
Fluid Flow ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Production Optimization ◽  
Oil Field ◽  
Injection Strategy ◽  
Field Development ◽  
Facies Classification ◽  
Resolution Graph

Machine learning is an algorithm based on pattern recognition and the concept that computers can learn without being programmed to perform specific tasks. Machine learning applications that are commonly used in the oil and gas companies are petrophysical estimation and well log classification, seismic structural identification, production forecasting, and artificial intelligence tasks. The goal of this study is to integrate machine learning workflows to evaluate how reservoir hydrocarbon distribution can help prospecting, field development, and production optimization, especially 4D seismic studies. Also to observe the fluid flow and to detect bypassed oil pockets changes during the production. The workflow consists of three phases: planning, execution, and delivery. The first phase consists of collecting and preprocessing wells, seismic and interpretation data. Once the plan is considered satisfactory, it will be followed by the execution that is started with data cleaning, processing, classification, and data validation. Machine learning methods are then deployed to build an electrofacies and reservoir distribution model for the Hugin Formation using Multi-Resolution Graph-Based Clustering (MRGC). After these models reach a satisfactory level, seismic attribute analysis is performed using Principal Component Analysis (PCA) and Democratic Neural Network Association (DNNA) to create a facies probability volume. The last step in this phase is to detect geobodies of oil sand and propose an infill well or injection strategy to enable the enhancement of the oil recovery. Once the machine learning results are satisfying, tthe status of the workflow will change from execution to the delivery phase to create the final project presentation. In our study, DNNA has demonstrated excellent prediction and facies classification to image a large volume encompassing some wellbores, changes in the fluid flow during production between baseline, and monitoring seismic surveys with a good Matthews correlation coefficient of 0.849554. It allows the operator to observe the dynamic processes in and around the reservoir to help the placement of infill wells more effectively, increas development and production success, reduce risk when following proposed infill wells. The integration of machine learning can also improve the understanding of hydrocarbons in the field. It shapes E&P business strategies in a way that may increase profit revenues, such as enhanced oil recovery of an effective and efficient infill well and optimizing an injection strategy.

scholarly journals An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

2021 ◽  
Vol 76 ◽  
pp. 82
Author(s):  
S. Mahdia Motahhari ◽  
Mehdi Rafizadeh ◽  
S. Mahmoud Reza Pishvaie ◽  
Mohammad Ahmadi
Keyword(s):  
Machine Learning ◽  
Enhanced Oil Recovery ◽  
Shannon Entropy ◽  
Oil Recovery ◽  
Oil Field ◽  
Unsupervised Machine Learning ◽  
Main Challenge ◽  
Ideal Method ◽  
Selection Of ◽  
Economic Criteria

Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran.

scholarly journals Improvement of oil field development using enhanced oil recovery methods

2021 ◽  
pp. 23-28
Author(s):  
G Moldabayeva ◽  
R Suleimenova ◽  
N Buktukov ◽  
M Mergenov
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Water Injection ◽  
Oil Field ◽  
Water Soluble ◽  
Sweep Efficiency ◽  
Conformance Control ◽  
Water Soluble Polymer ◽  
Field Development ◽  
Water Cut

Purpose. To develop a technology to increase the oil recovery of formations using injection of polymer compositions. Methodology. For this study, practical methods were used such as enhanced oil recovery using stimulating technologies, technology using polymer systems based on a water-soluble polymer acrylamide, and emulsion-polymer technology. To achieve the conformance control, which was a prerequisite for testing, a thorough selection of wells was carried out, as well as an analysis of their hydrodynamic connection. Findings. As a result of using the method for limiting water inflows in the development of oil-bearing formations, redistribution of filtration channels, and a decrease in the production of fossil water as well as stabilisation of water cut were achieved. Originality. The scientific novelty of the study is the withdrawal of wells that are able to redistribute the volume of water injection at perforation intervals. Increased sweep efficiency and pressure at the wellhead at the beginning and at the end of the conformance control indicate a decrease in the conductivity of high-permeability formation intervals. Practical value. Application of the proposed technology for limiting water inflows will make it possible to develop low-permeability interlayers with filtration flows. The wells brought to a stable production rate during the study will ensure a decrease in formation water production and the water cut of the produced products, as well as stabilisation of the water cut over a certain period.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

FACIES CLASSIFICATION OF A COMPLEX RESERVOIR USING MACHINE LEARNING: CASE STUDY FROM VOLCANIC FORMATION, THE YURIHARA OIL FIELD, JAPAN

2021 ◽  
Author(s):  
Yuki Maehara ◽  
◽  
Takeaki Otani ◽  
Tetsuya Yamamoto ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Pillow Lava ◽  
Gaussian Mixture ◽  
Oil Field ◽  
Well Logs ◽  
Depth Interval ◽  
Volcanic System ◽  
Facies Classification ◽  
Volcanic Formation ◽  
Borehole Image

Lithological facies classification using well logs is essential in the reservoir characterization. The facies are manually classified from characteristic log responses derived, which is challenging and time consuming for geologically complex reservoirs due to high variation of log responses for each facies. To overcome such a challenge, machine learning (ML) is helpful to determine characteristic log responses. In this study, we classified the lithofacies by applying ML to the conventional well logs for the volcanic formation, onshore, northeast Japan. The volcanic formation of the Yurihara oil field is petrologically classified into five lithofacies: mudstone, hyaloclastite, pillow lava, sheet lava, and dolerite, with pillow lava being predominant reservoir. The former four lithofacies are the members of the volcanic system in Miocene, and dolerite randomly intruded later into those. Understanding the distribution of omnidirectional tight dykes at the well location is important for the estimation of potential near-lateral seal distribution compartmentalizing the reservoir. The facies are best classified by core data, which are unfortunately available in a limited number of wells. The conventional logs, with the help of the borehole image log, have been used for the facies classification in most of the wells. However, distinguishing dolerite from sheet lava by manual classification is very ambiguous, as they appear similar in these logs. Therefore, automated clustering of well logs with ML was attempted for the facies classification. All the available log data was audited in the target well prior to applying ML. A total of 10 well logs are available in the reservoir depth interval. To prioritize the logs for the clustering, the information of each log was first analyzed by Principal Component Analysis (PCA). The dimension of variable space was reduced from 10 to 5 using PCA. Final set of 5 variables, gamma-ray, density, formation photoelectric factor, neutron porosity, and laterolog resistivity, were used for the next clustering process. ML was applied to the selected 5 logs for automated clustering. Cross-Entropy Clustering (CEC) was first initialized using k-means++ algorithm. Multiple initialization processes were randomly conducted to find the global minimum of cost function, which automatically derived the optimized number of classes. The resulting classes were further refined by the Gaussian Mixture Model (GMM) and subsequently by the Hidden Markov Model (HMM), which takes the serial dependency of the classes between successive depths into account. Resulting 14 classes were manually merged into 5 classes referring to the lithofacies defined by the borehole image log analysis. The difference of the log responses between basaltic sheet lava and dolerite was too subtle to be captured with confidence by the conventional manual workflow, while the ML technique could successfully capture it. The result was verified by the petrological analyses on sidewall cores (SWCs) and cuttings. In this study, the automated clustering with the combination of several ML algorithms was demonstrated more efficient and reasonable facies classification. The unsupervised learning approach would provide supportive information to reveal the regional facies distribution when it is applied in the other wells, and to comprehend the dynamic behavior of the fluids in the reservoir.

Experimental Design and Evaluation of Surfactant Polymer for a Heavy Oil Field in South of Sultanate of Oman

2021 ◽  
Author(s):  
Ali Reham Al-Jabri ◽  
Rouhollah Farajzadeh ◽  
Abdullah Alkindi ◽  
Rifaat Al-Mjeni ◽  
David Rousseau ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Heavy Oil ◽  
History Matching ◽  
Oil Field ◽  
Reservoir Rock ◽  
Sultanate Of Oman ◽  
Industrial Chemicals ◽  
Oil Viscosity ◽  
Injection Strategy ◽  
Operational Conditions

Abstract Heavy oil reservoirs remain challenging for surfactant-based EOR. In particular, selecting fine-tuned and cost effective chemical formulations requires extensive laboratory work and a solid methodology. This paper reports a laboratory feasibility study, aiming at designing a surfactant-polymer pilot for a heavy oil field with an oil viscosity of ~500cP in the South of Sultanate of Oman, where polymer flooding has already been successfully trialed. A major driver was to design a simple chemical EOR method, to minimize the risk of operational issues (e.g. scaling) and ensure smooth logistics on the field. To that end, a dedicated alkaline-free and solvent-free surfactant polymer (SP) formulation has been designed, with its sole three components, polymer, surfactant and co-surfactant, being readily available industrial chemicals. This part of the work has been reported in a previous paper. A comprehensive set of oil recovery coreflood tests has then been carried out with two objectives: validate the intrinsic performances of the SP formulation in terms of residual oil mobilization and establish an optimal injection strategy to maximize oil recovery with minimal surfactant dosage. The 10 coreflood tests performed involved: Bentheimer sandstone, for baseline assessments on large plugs with minimized experimental uncertainties; homogeneous artificial sand and clays granular packs built to have representative mineralogical composition, for tuning of the injection parameters; native reservoir rock plugs, unstacked in order to avoid any bias, to validate the injection strategy in fully representative conditions. All surfactant injections were performed after long polymer injections, to mimic the operational conditions in the field. Under injection of "infinite" slugs of the SP formulation, all tests have led to tertiary recoveries of more than 88% of the remaining oil after waterflood with final oil saturations of less than 5%. When short slugs of SP formulation were injected, tertiary recoveries were larger than 70% ROIP with final oil saturations less than 10%. The final optimized test on a reservoir rock plug, which was selected after an extensive review of the petrophysical and mineralogical properties of the available reservoir cores, led to a tertiary recovery of 90% ROIP with a final oil saturation of 2%, after injection of 0.35 PV of SP formulation at 6 g/L total surfactant concentration, with surfactant losses of 0.14 mg-surfactant/g(rock). Further optimization will allow accelerating oil bank arrival and reducing the large PV of chase polymer needed to mobilize the liberated oil. An additional part of the work consisted in generating the parameters needed for reservoir scale simulation. This required dedicated laboratory assays and history matching simulations of which the results are presented and discussed. These outcomes validate, at lab scale, the feasibility of a surfactant polymer process for the heavy oil field investigated. As there has been no published field test of SP injection in heavy oil, this work may also open the way to a new range of field applications.

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