ProOpter: An advanced platform for production analysis and optimization

The present study conducted a Systematic Literature Review (SLR) on the influence of Big Data in the definition of Key Performance Indicators (KPIs) in logistics and their impact on customer satisfaction. The reviewed literature showed numerous studies on the use of Big Data in logistics; however, there were few efforts that synthesized aspects such as defined KPIs, developed methods, and implemented tools. The objective was to explore and analyze how Big Data supported the definition of logistics KPIs and its influence on customer satisfaction. The methodology encompassed three stages: planning, implementation, and results, following Kitchenham's approach. A review protocol was established with key research questions and inclusion and exclusion criteria. Systematic searches were conducted in relevant databases, and pertinent studies were selected. The results revealed that the definition of KPIs varied according to context and industry; 23 KPIs, 9 Big Data methods, and 7 IT tools were identified. Finally, the conclusions emphasized the importance of adapting logistics KPIs to business priorities and the relevance of Big Data in their definition to improve both decision-making and customer satisfaction.

Surface network production flow in the oil and gas industry is a complex process influenced by fluid properties, pipeline configurations, and operational conditions. Traditionally, this estimation is performed using nodal analysis or empirical correlations, both of which rely on physical assumptions and theoretical models. However, these conventional methods often introduce significant assumptions and simplifications that may not fully capture the complexities of real-world production behavior. In this work, a Data-Driven approach was used, where machine-learning models analyze historical production data to learn patterns without relying on physics-based assumptions. Trends and relationships are identified solely from historical data, eliminating the need for complex physical equations or empirical formulas. The objective is to use pure data analytics to build an accurate oil-production prediction model. This study explores the application of machine-learning techniques for oil and gas production forecasting, integrating exploratory data analysis (EDA) and predictive modeling using historical production data from different oil platforms. A Python-Dash dashboard was developed for historical monitoring of key production metrics, featuring dynamic visualization and seamless data integration. Prediction of oil, gas, and water rates was carried out using three regression models, Random Forest, XGBoost, and Support Vector Regression (SVR) with data preprocessing ensuring numerical consistency and null-free values for effective training. Model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² score, with XGBoost identified as the best-performing model. Predictions on the validation dataset were visualized through line plots, highlighting production trends and fluctuations. The cleaned and forecasted data were exported for further analysis and decision-making. This approach demonstrates the potential of machine learning in optimizing oilfield operations and enhancing resource management through accurate production forecasting.

Decline Curve Analysis (DCA) is one of the most common tools to predict oil and gas well performance and determine an estimated ultimate recovery based on historical production data. Probabilistic approaches have evolved to provide a measure of uncertainty in such estimates. However, engineers have held onto the belief that such quantification of uncertainty is largely subjective. Consequently there is an innate reluctance to adopt probabilistic methodologies owing to the assumption of prior knowledge of the relevant parameters and reservoir properties distributions. The objective of this paper is to explicate the development of an improved probabilistic approach to estimate reserves and well performance based on historical production data. This methodology precludes the assumption of prior distributions by adopting a bootstrap workflow that is implemented to construct probabilistic estimates with specified confidence intervals from historical production data. It is a statistical approach to assess the uncertainty of estimates objectively, removing the subjective nature of prior assumptions. We shall discuss an automated selection criteria workflow for time series and forecast periods resulting in more robust and accurate DCA. The methodology1 abides by a "more rigorous model-based bootstrap algorithm" that encapsulates the appropriate steps to preserve the inherent characteristics of a time series data set that show an overall decline trend. The paper also explores other potential advanced analytical techniques to gather knowledge from historical upstream data so as to identify wells within an asset that require some remediation based on real-time performance observations. Cluster analysis could improve engineers’ convenience in analyzing wells by separating them into groups based on decline curve shapes (patterns) and other reservoir properties. We shall define some processes that implement soft computing techniques to aggregate the DCA results with disparate upstream data via a data driven methodology. Pattern recognition and classification are important steps that underpin data mining, and we shall explicate a suite of workflows to enable a well optimization solution.

The assessment of the nation's undiscovered petroleum resources by the US Geological Survey uses the identification of geologically defined plays and the estimation of the size distribution and number of undiscovered fields in each play from historical production data. The Appalachian basin contains six major oil and gas plays, but in contrast to more recently drilled basins, the plays of the mature Appalachian basin lack a complete geologic and production database. Data such as reservoir porosity, permeability, thickness, saturation, drilling depth, and historical production are either unknown, scattered in the literature, deeply buried in company and government files, or exist in incomplete regional government and private commercial data files. In an effort to increase the database and to improve the assessment of the basin, an informal atlas was initiated to test the feasibility of acquiring additional data and the utility of these data in improving the assessment. Critical to the assessment procedure and noticeably incomplete in the basin are the historical cumulative production data necessary for analyzing the distribution of the number of fields and field sizes for the individual plays. Of the Appalachian basin plays, the Oriskany Sandstone play was initially selected and encouraging amount of previously unidentified productionmore » data is available. Achieving the maximum amount of the quality of data for each play in the atlas will require the cooperation of those working in the Appalachian basin.« less

There is a growing interest over the last decades in the field of autonomous island grids that is driven mainly by climate reasons. The common objective among the members of the European Union (EU) is the increase of Renewable Energy Sources (RES) penetration in the energy mixture, as well as turning the grid into a smart grid. Consequently, more and more state-of-the-art solutions are being proposed for the electricity generation and the optimization of the energy system management, taking advantage of innovations in all energy related sectors. The evaluation of all available solutions requires quantitative assessment, through the adoption of representative Key Performance Indicators (KPIs) for the projects that are related to smart grid development in isolated energy systems, providing the relevant stakeholders with a useful comparison among the proposed solutions. The evaluation approach that is described in this paper emphasizes the role of the various stakeholder groups who face the proposed solutions by different points of view. Apart from the domains of interest that are also observed in previous approaches, the proposed list also contains a set of legal KPIs, since the regulatory framework can either represent a serious barrier or grant a strong incentive for the implementation of state-of-the-art energy technology and grid management solutions in different countries.

Production decline in offshore oil and gas platforms is influenced by operational conditions, flow assurance challenges, and well-specific behaviour. Among these challenges, wax deposition is a recurring issue that restricts flow and degrades production performance if not proactively managed. This study proposes a data-driven framework that integrates machine learning and deep learning techniques to predict production flow behaviour and wax deposition in a mature offshore platform experiencing gradual decline. Historical production data, operational parameters, well test results, and event logs were analyzed to develop predictive models for liquid production rate, gas–oil ratio, water cut, and wax deposition. For wax prediction, three tree-based machine learning models were evaluated. LightGBM achieved the best performance with an R2 of 0.902 and a mean squared error of 11.18, effectively capturing gradual wax accumulation while showing limitations during extreme operational changes. For flow rate forecasting, both machine learning regression models and a multivariate long short-term memory (LSTM) model were implemented. XGBoost delivered the highest overall accuracy among machine learning models with an R2 of 0.984. However, per-well evaluation revealed simpson’s paradox and scale effects, where strong aggregate performance masked inconsistencies at the well level. The multivariate LSTM model better captured temporal dependencies and improved long-term forecasting, although recursive prediction led to drift beyond approximately 20 days. The findings demonstrate the potential of AI-based predictive analytics to support wax management and production optimisation in offshore operations. The proposed framework enables continuous flow estimation and early detection of production bottlenecks. The study further highlights the importance of well-specific modelling, extended historical datasets, and robust forecasting strategies to enhance reliability in offshore production applications.

436 Background: Tennessee Oncology is a large community oncology practice with over 30 clinics providing 89,000 treatments per year across Tennessee and northern Georgia. Tennessee Oncology’s scheduling application was unable to optimally schedule treatment appointments. This scheduling gap was causing frequent patient delays and employee extended hours. Tennessee Oncology partnered with Smirta, Inc., to develop a data and artificial intelligence (AI) driven scheduling overlay platform that would optimize and simplify cancer treatment scheduling as well as predict scheduling patterns and resource needs. Methods: Named OncoSmart, the scheduling optimization platform ingests historic scheduling data, detailed clinic configuration data including provider and nursing schedules, and available resource data such as treatment room chairs. Utilizing AI, the platform generates optimal scheduling recommendations matching the specific set of services that need to be scheduled. The platform overlays the current scheduling app and provides dynamic, real-time recommendations based on current resource (treatment room, provider, etc.) schedule availabilities and bookings. Tennessee Oncology piloted the scheduling optimization platform at 1 clinic and has currently expanded the pilot to 12 additional clinics. Results: After various ranges of clinic pilot times (6 months to 2 years), Tennessee Oncology treatment volumes have increased by 7%. In parallel to this increase, the optimization platform has helped decrease extended hours by over 32%. The original pilot site has shown major improvement in all 4 primary key performance indicators (KPI): treatment volume +12%; Chair utilization +12%; treatment delay -9%; extended hours -82%. Additionally, using the platform’s predictive analytics capabilities, analyses have been completed to generate optimal treatment scheduling patterns as well as optimal treatment nursing staffing models. Conclusions: Within a short period after deployment, Smirta Inc’s OncoSmart has helped Tennessee Oncology identify better treatment scheduling options for these 13sites. The scheduling optimization platform has proven to be very effective in identifying optimal treatment scheduling strategies and in identifying critical resource bottlenecks. The platform’s clinic management, optimization, nurse assignment, business intelligence, and resource management modules has empowered Tennessee Oncology to better manage critical clinical resources and reduce staff overtime during a period of growth.

Summary Selecting a set of deterministic (e.g., P10, P50, and P90) models is an important and difficult step in any uncertainty quantification workflow. In this paper, we propose to use multiobjective optimization to find a reasonable balance between the often conflicting features that must be captured by these models. We embed this approach into a streamlined uncertainty quantification workflow that seamlessly integrates multirealization history matching, production forecasting with uncertainty ranges, and representative deterministic model selection. Some uncertain parameters strongly impact simulated responses representing historic (production) data and are selected as active parameters for history matching, whereas others are important only for forecasting. An ensemble of conditional realizations of active history-matching parameters is generated in the multirealization history-matching stage using a distributed optimizer that is integrated with either a randomized maximum likelihood (RML) or a Gaussian mixture model (GMM). This ensemble is extended with unconditional realizations of forecast parameters generated by sampling from their prior distribution. Next, the petroleum engineer must select primary and secondary key performance indicators and identify models from this ensemble that optimally generate P10, P50, and P90 values for these indicators. In addition to matching target values of these key performance indicators (e.g., cumulative oil/gas/water production and recovery factor), selected representative models (RMs) typically must satisfy regulatory or management-imposed requirements or constraints (e.g., the value of some key parameters must be within a user-specified tight range). It can be quite difficult to find a set of RMs that satisfy all requirements. Even more challenging, some requirements may conflict with others, such that no single model can satisfy all requirements. To overcome these technical difficulties, we propose in this paper to formulate different requirements and constraints as objectives and develop a novel two-stage multiobjective optimization strategy to find a set of Pareto optimal solutions based on the concept of dominance. In the first stage, we propose selecting P10, P50, and P90 candidates by minimizing the indicator mismatch function and constraints violation function. In the second stage, we propose selecting combinations of P10, P50, and P90 candidates from the previously generated posterior ensemble, obtained in the first stage by optimizing other objectives. One or more sets of RMs can then be selected from the set of optimal solutions according to case-dependent preferences or requirements. Because the number of P10, P50, and P90 candidates selected in the first stage is much smaller than the number of all samples, the proposed two-stage approach performs much more efficiently than directly applying the traditional multiobjective optimization approach or clustering-based approaches. The proposed method is tested and validated against a realistic example. Our results confirm that the proposed method is robust and efficient and finds acceptable solutions with no or minimal violations of constraints. These results suggest that our advanced multiobjective optimization technique can select high-quality RMs by striking a balance between conflicting constraints. Thus, a better decision can be made while running much fewer simulations than would be required with traditional methods.

Recent advancements in the field of natural language processing (NLP) and machine learning has allowed for the potential to ingest decades of field history and heterogeneous production records. This paper proposes an analytics workflow that leverages artificial intelligence to process thousands of historical workover reports (handwritten and electronic), extract important information, learn patterns in production activity, and train machines to quantify workover impact and derive best practices for field operations. Natural language processing libraries were developed to ingest and catalog gigabytes of field data, identify rich sources of workover information, and extract workover and cost information from unstructured reports. A clustering based architecture was developed and trained to categorize documents based on free text describing the activities found in reports. This machine learning model learnt the pattern and context of repeating words and was able to cluster documents with similar content together. This enabled the user to find a category of documents e.g. workover intervention reports instantaneously. Statistical models were built to determine return on investment from workovers and rank them based on production improvement and payout time. Today, 80% of an oilfield expert's time can be spent manually organizing data. When processing decades of historical oilfield production data spread across both structured (production timeseries) and unstructured records (e.g., workover reports), experts often face two major challenges: 1) How to rapidly analyze field data with thousands of historical records. 2) How to use the rich historical information to generate effective insights to take the proper actions to optimize production. In this paper, we analyzed multiple field datasets in a heterogeneous file environment with 20 different file formats (PDF, Excel, and other formats), 2,000+ files, production history spanning 50+ years across, and 2,000+ producing wells. Libraries were developed to extract files from complex folder hierarchies, machine learning architectures assisted in finding the workover reports from the myriad documents. Information from reports was extracted through Python libraries and optical character recognition technology to build master data source with production history, workover and cost information. The rich dataset was then used to analyze episodic workover activity by well and compute key performance indicators (KPIs) to identify well candidates for production enhancement. The building blocks included quantifying production upside and calculating return of investment for various workover classes. O&G companies have vast volumes of unstructured data and use less than 1% of it to uncover meaningful insights about field operations. Our workflow describes a methodology to ingest both structured and unstructured documents, capture knowledge, quantify production upside, understand capital spending, and learn best practices in workover operations through an automated process. This process helps optimize forward operating expense (OPEX) plans with a focus on cost reduction and shortened turnaround time for decision making.

Quality of Service Management (QoSM) is a new task in IT-enabled enterprises that supports monitoring, collecting and predicting QoS data. QoSM solutions must be able to efficiently process runtime events, compute and pre dict QoS metrics, and provide real-time visibility and prediction of key perform ance indicators (KPI). Currently, most QoSM systems focus on moni tor ing of QoS constraints, i.e., they report what has been happened. In a way, this provides the awareness of past developments and sets the basis for decisions. However, this kind of knowledge is afterwit. For example, it cannot provide early warnings to prevent the QoS degradation or the violation of commitments. In this paper, we move one step forward to provide QoS prediction. We argue that performance metrics and KPIs can be predicted based on historical data. We present the design and implementation of a novel event-driven QoS prediction system. Integrated into the SOA infrastructure at large, the prediction system can process operational service events in a real-time fashion, in order to predict or refine the prediction of metrics and KPIs.

In a recent work by Lee and Datta (1994), the feasibility of applying a model-based dynamic estimation and control technique was tested. It was shown that reliable monitoring and control of the Kappa number can be achieved, even in the presence of significant errors in the initial state estimates, given that the model parameters can be adapted efficiently. However, since the available fundamental pulping models are of very high-order involving many parameters, simultaneous update of the states and parameters resulted in an ill-conditioned estimation problem, leading to unacceptably slow convergence or divergence. This indicated a need for a reduced-order model that captures the essential aspects of the pulping dynamics. Development of such a model and testing of its suitableness for online use are topics of this paper. We verify the adequacy of the proposed reduced-order model and its online adaptability by applying a standard model-based nonlinear estimation and predictive control technique.

We present a robust control system and methodology for physics-informed artificial intelligence (PAI) used to optimize and improve oil recovery, demonstrated in the Yates Field operated by the Kinder Morgan CO2 company. The system consists of a robust control system (referred to as Dynamic Feedback Loop, or DFL) equipped with novel hydrocarbon sensors that measure oil concentration and other parameters continuously and simultaneously on a set of producing wells. The goal of this system is to optimize operational parameters (e.g. choke valve settings, injection rates) to reach specific target metrics of production (e.g. maximizing produced oil while minimizing produced gas).The key element of our approach is the use of a multi-layer artificial neural network (deep neural network, or DNN, to be specific) that extracts physics-based parameters from the real-time measurements and predicts relevant parameters of the DFL control system. DNNs are prone to overfitting in training, making them ineffective in unfamiliar or challenging situations outside the training dataset. To overcome this problem, we have developed a physics-informed robust neural network technique, where the reservoir physics and sensor data are used to train DNN representations of the key physical parameters. Typically, only simplified physical models are developed using available geostatic or historical production data. Also, due to the dynamic nature of these systems, the accuracy of the models often changes over time. To improve predictive capability of the model, we combine the DNN with the system-theoretic robust control concepts based on physics with a model uncertainty formulation. The concept was first validated using a combination of simulations, isolated sensor data and analyses based on sets of historic production data.A study using historic production data on Kinder Morgan's Yates Field Unit (YFU) 4045 Pilot (3 producing wells) indicates application of the DFL system results in an increase in cumulative production of up to 35% per year, compared to what is obtained through a traditional (fixed-point) control system. Currently, the DFL is being field-tested on a different set of wells in the Yates Field, instrumented with the novel hydrocarbon sensors that generate continuous and simultaneous production data.

The objective of this study is to summarize a proven solution workflow to address the challenges to handle the high volume of well tests daily incorporating information from operational activities, and especially, potential delays and errors in validation impacting other dependent business processes. The proposed solution aims to reduce processing time, minimize human error, and enhance accuracy in well test analysis. Having up-to-date and reliable well test data, engineers can improve engineering workflows, and optimize production. The solution covers data consumption, data preparation, machine learning (ML) solution, cooperating with dependent business processes, deployment and retrain strategy. The ML solution learns from historical well test data with accepted and rejected flag to build a rule-based deterministic ML model to automatically validate and detect the invalid well test with probability. The solution does not only consume structure data but also textual data with natural language processing (NLP), such as well test comments provided by well testing engineers and operational activities in Daily Operational Reports (DORs). Data consumption, operational activities, dependent workflow control are customizable based on different projects. Retrain strategy is based on model prediction accuracy trend and defined during deployment. The solution triggers insights with confidence scores, suggesting acceptance/rejection or review of new well tests. Early detection of possible rejections enables timely actions, including retesting if necessary. The solution was implemented and significantly reduces well test validation time from weeks to hours, enhancing the accuracy of production analysis and optimizations. The data-driven approach offers flexibility and adaptability to meet operation needs, presenting a robust alternative to rule-based validation. By integrating ML and NLP, the solution provides a comprehensive and efficient framework for well test validation, improving decision-making and ensuring compliance with Standard Operation Procedure (SOP). This study introduces a novel approach to well test validation by leveraging ML and NLP. By considering both historical data and manual operational event inputs from engineers, the solution enhances the accuracy and efficiency of the validation process. It contributes to improved production performance analysis, diagnostics, and issue detection. The solution deployment can be customized and adaptable to different data storage and availability, to automate well test validation process in the oil and gas industry.

Reservoir model history matching is a complex, time-consuming, and resource intensive process that needs to be carried out carefully for building reliable predictive tools to manage Oil & Gas assets. Reservoir models encompass detailed geological description representing subsurface heterogeneities that influence its dynamics. To intelligently manage and preserve the complexity of the reservoir models, an artificial intelligence, Progressive-Recursive Self-Organizing Maps (PR-SOM), algorithm was developed. PR-SOM is an unsupervised artificial intelligence neural network algorithm that classifies the reservoir grid cells into progressive reservoir parameters to identify similarly adjoining regions. The algorithm explores and identifies model geo-bodies with similarities and dissimilarities in a progressive and recursive manner. This allows history matching to be conducted on much smaller subsets of the reservoir model of similar geological features. In this work, an artificial intelligence (AI) algorithm was applied, first, to guide the reservoir-wide history match processes. Next, the algorithm was applied to fine-tune well performance using information form well testing and historical data. The algorithm uses both static properties (permeability, rock quality indices, porosity, flow zonation … etc.) and dynamic properties (pressure or saturation) to construct similarities matrix. The results show that the clusters’s growth is progressive, controlled and quality assured by accounting for the controlling reservoir parameters. The number of mapped regions (clusters) is determined by optimizing the similarity matrix recursively. The quality of the global reservoir history match shows the effectiveness of the algorithm, better quality matching for historical production data, and fewer iterations (i.e. less simulation runs). The process is repeated to calibrate the reservoir model near wellbores by limiting the AI algorithm to only the drainage regions seen from well tests and historical data. The results show that employed AI-guided history matching revealed similarities and dissimilarities in the reservoir model. That not only enhance field and well match, but also allowed us to maintain the heterogeneity contrasts inherited from the Earth model. The advanced algorithm was successfully used to assess the extent of geological heterogeneity and its impact on reservoir dynamics, to enhance history match quality, minimize human interaction, and to reduce computational requirements.

The multiple model-based control (MMBC) technique utilizes knowledge of nominal robot dynamics and principles of Bayesian estimation to provide payload-independent trajectory tracking accuracy. The MMBC algorithm is formed by augmenting a model-based controller with a form of multiple model adaptive estimation (MMAE). The MMAE uses perturbation models of the robot dynamics and joint angle measurements to provide an estimate of the payload parameters required to minimize trajectory tracking errors. The model-based controller combines a priori knowledge of robot structure with the payload estimate to produce the multiple models of the manipulator dynamics required to maintain controller accuracy. Extensive simulation studies on the first three links of a PUMA-560 have been validated by experimental evaluation. It is concluded that MMBC provides a unique solution to the problem of maintaining trajectory tracking accuracy in uncertain payload environments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>