Machine Learning for New Product Forecasting

Static pressure is one of the very important parameters for reservoir engineering, it gives us precious information about our reservoir, such as drive mechanisms, quantities of hydrocarbon in place, patterns, communication between wells, fluid behavior in the reservoir, as consequence, the measurement of this parameter must be conducted on periodical basis, to appropriately know the field and build a good model of reservoir. The advantage of this study can complete other studies that concentrate only on the oil production rate forecasting like Data Driven Production Forecasting Using Machine Learning [1], Production Forecasting in Conventional Oil Reservoirs Using Deep Learning [2], Machine Learning Prediction Versus Decline Curve Prediction: A Niger Delta Case Study [3], Decline Curve Analysis for Production Forecasting Based on Machine Learning [4] ……, in addition of static pressure evolution of wells. For instance, we can optimize through this study a number of conducted tests to measure static pressure which will minimize operating costs and the probability of accidents occurring the operations, also reduce the shutdown time of wells for completion purpose of such measurement, in addition to the possibility of using this model for other analogue wells that do not have enough pressure measurement, without the need for time and extensive study. Besides, multivariate polynomial regression machine learning algorithm has been developed in this study to predict the evolution of static pressure for existing oil wells.

Horizontal wells placement and production forecast of unconventional assets play critical roles in the success or failure of any given operation. Traditional reservoir simulation workflows are ineffective for unconventional assets and often lead to erroneous results in addition to being both cost- and time-prohibitive. This paper presents a streamlined data-driven workflow of optimal horizontal target placement in unconventionals coupled with deep-learning (DL) techniques to accurately forecast production rates. The presented framework relies on automated geologic and engineering workflows to map remaining oil, advanced algorithms to perform an optimized global search with 3D pay tracking, and statistical and DL-based techniques to assess neighborhood performance and geologic risks. The workflow handles multiple types of constraints, including configuration constraints like length, azimuth, and deviation range, as well as path constraints like zone, baffle, and fault-surface crossing. For production forecasting, we developed an Encoder-Decoder Long Short-Term Memory Networks (LSTM) architecture. The model combines three types of inputs, and the output is multi-step multiphase forecasts. The input data for each well consists of time-variant information (i.e., historical production), static well features (e.g., geology and spacing parameters), and known-in-advance control variables. In addition, we use quantile regression to estimate the confidence interval around the forecasts. The post-prediction process then aggregates the results by combining economic analysis, risk assessment, and operational restrictions. We successfully deployed this technology to a giant unconventional play in North America with more than 4000 wells. We identified an inventory of 700 potential horizontal targets with optimized completion design; 90 of them were in the low-risk category with estimated additional reserves of 55.6 MMSTB. After establishing a database of tens of thousands of historical hydraulic fractures using advanced data mining techniques, we defined key impacting features using advanced feature engineering techniques (combining key fracture features as well as deconvoluting geological effects using unsupervised learning). We then developed a predictive DL model using selected features and quantified the impact of each feature on the production of each well. Moreover, the model generates a probabilistic production forecast that allows operators to model future activities. This technology provides a robust, streamlined, fast, and accurate approach to identifying optimal horizontal well targets as well as examining historical hydraulic fracturing performance, using state-of-the-art machine learning workflows augmented by domain expertise. It provides a domain-infused feature engineering process, absorbed by an explainable DL architecture. It uncovers non-linear dependences on well features and provides fast prediction and uncertainty quantification.

Accurate oil and gas production forecasting is essential for optimizing field development and operational efficiency. Steady-state capacity prediction models based on machine learning techniques, such as Linear Regression, Support Vector Machines, Random Forest, and Extreme Gradient Boosting, effectively address complex nonlinear relationships through feature selection, hyperparameter tuning, and hybrid integration, achieving high accuracy and reliability. These models maintain relative errors within acceptable limits, offering robust support for reservoir management. Recent advancements in spatiotemporal modeling, Physics-Informed Neural Networks (PINNs), and agent-based modeling have further enhanced transient production forecasting. Spatiotemporal models capture temporal dependencies and spatial correlations, while PINN integrates physical laws into neural networks, improving interpretability and robustness, particularly for sparse or noisy data. Agent-based modeling complements these techniques by combining measured data with numerical simulations to deliver real-time, high-precision predictions of complex reservoir dynamics. Despite challenges in computational scalability, data sensitivity, and generalization across diverse reservoirs, future developments, including multi-source data integration, lightweight architectures, and real-time predictive capabilities, can further improve production forecasting, addressing the complexities of oil and gas production while supporting sustainable resource management and global energy security.

In the absence of 3D dynamic models for conceptual reservoir development and production forecasting, reservoir engineers are left with only one fallback option – the use of tank material balance models which may not fully capture the physics of flow in complex reservoir systems. In order to reasonably account for the varying factors that may affect production performance, a bit of ingenuity must be applied. This paper describes the workflow involved in developing a comprehensive integrated asset model that is able to predict production performance under different reservoir configurations. First, using available petrophysical information, a method is presented to estimate recovery factor ranges and the corresponding residual saturations which is then used to constrain material balance models in prediction of ultimate recovery. The reservoir is then divided into drain sections with conceptualized target wells and the entire reservoir system is modeled as a connected multi-tank model with varying in-place volumes. The final steps involve building an integrated asset model consisting of wells and facility equipment and generating production forecasts. In this study, key factors that impact the complex multi-tank model behavior and the resulting production forecasts are outlined. Boundaries were then set for these factors and multiple simulation runs generated for proxy model building using Neural Networks. However, it was noted that most of these factors were temporal and did not influence the overall cumulative production overtime. Hence for this study a fixed time period was used to evaluate uncertainties and generate forecast ranges. This paper will be beneficial to reservoir engineers tasked with developing strategies for reservoir exploitation and forecasting pre-drill production profiles. It will be of special interest to engineers inclined to automation, script writing and the use of machine learning in subsurface analysis.

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.

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development.

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process. Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented. A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.

Advances in the fields of information technology, computation, and predictive analytics have permeated the energy industry and are reshaping methods for exploration, development, and production. These technologies can be applied to subsurface data to reliably predict a host of properties where only few are available. Among the numerous sources of subsurface data, rock and fluid analysis stand out as the means of directly measuring subsurface properties. The challenge in this work is to maximize information gain from legacy pdf reports and unstructured data tables that represented over 70 years of laboratory work and investment. The implication of modeling this data into an organized data store means better assessment of economic viability and producibility in frontier basins and the capability to identify bypassed pay in old wells that may not have rock material. This paper presents innovative and agile technologies that integrate data management, data quality assessment, and predictive machine learning to maximize the company asset value using underutilized legacy core data. The developed machine learning algorithms identify potential outliers, benchmark the valuable data against current industry standards, increase the confidence in data quality and avoid amplifying error in predicting reservoir properties. The workflow presented in the paper is expected to reduce uncertainties in subsurface studies caused by limited core data, improper analog selection, high cost, limited time for acquiring new cores, and long delivery times of core analysis data. The workflow reduces the requirement for subsurface formation evaluation rework as new data becomes available at later project stages resulting in optimized field development. The workflow enhanced by machine learning also improves the prediction and propagation of reservoir properties to uncored borehole sections. In conclusion, managing legacy core data and transforming it to generate new subsurface insights are critical step to establish a reliable database in support of business excellence and the digitalization journey. Innovative machine learning tools continue to unlock new values from legacy core data that significantly impact the entire reservoir life cycle including reserves booking, production forecasting, well placement, and completion design.

Production forecast and optimization for parent-child well pattern in unconventional reservoirs

This paper aims to present an integrated approach for well forecasting that combines advanced Machine Learning (ML) techniques to provide accurate oil, gas and water predictions for existing and new wells in oil reservoirs. Well forecasting is a critical task in the oil and gas industry for optimizing production and maximizing recovery from reservoirs. Traditionally, decline curve analysis has been widely used for well forecasting, but it has limitations in complex reservoirs or wells with uncertain decline trends. The proposed approach overcomes these limitations by utilizing advanced Machine Learning techniques that leverage complex real data for improved production forecasting. By incorporating ML algorithms, the proposed approach can provide more accurate and reliable predictions, particularly in challenging reservoir conditions where traditional methods may fall short. The proposed approach for well forecasting involves several key steps. Firstly, historical data of wells, including production, choke, well head flowing pressure, producing days, injection data, well connection properties, and 3D properties, are collected and preprocessed. Next, 2D properties, such as well location, spatial coordinates, and spatial attributes, are extracted from the 3D properties of the reservoir using geospatial algorithms. Then, for existing wells an ensemble of ML supervised algorithm is trained well by well with enough historical data, and nearby wells are used to train non mature wells. For future new wells, historical data plus geological 2D properties is used to create a model per reservoir. This involves collecting and preprocessing data from nearby wells in the vicinity of the new drilling location to obtain relevant properties. Uncertainty intervals are calculated using quantiles to quantify the uncertainty in the predictions. The integrated well forecasting approach presented in this paper provides a comprehensive solution for predicting well production in oil reservoirs. We used 2 different approaches to predict oil, gas and water production depending on the availability of historical data. Our approach allowed accurate predictions in complex reservoirs where wells have no clear decline trends. The use of uncertainty intervals provides a measure of prediction uncertainty, allowing for informed decision-making.

We are seeing an uptrend in the instrumentation of legacy wells across the world, as costs lower and business cases become obvious. Several interesting technology applications have been showcased lately about retrofitting instrumentation and control technology in legacy wells. Advances on the digital front, where data science and engineering analytics are becoming more embedded in regular production monitoring and optimization processes, have been widespread. On the gas lift side, developments in surface controllable gas lift valves, which can be deployed during the completion phase, or retrofitted in existing mandrels through well intervention, have proceeded apace. This technology can bring significant improvement in the monitoring and management of wells because the operating envelope can be significantly expanded. Other technology developments involve the use of alternative data. As the saying goes, somebody’s noise is somebody else’s data. A use case of alternative data for rod pumps uses edge computing to process electric measurements and extract features using signal processing and machine learning. This can be used to create synthetic dynamometer cards used to optimize the wells in real time and predict failures ahead of time. Similar techniques could be applicable for electric submersible pumps. Distributed acoustic sensing (DAS) has many applications, but it is challenging to fully exploit the capability because of the sheer amount of raw data coming from these sensors. The data need to be compressed using feature-extraction algorithms. Each use case may require data over different regions of the frequency spectrum covered by the sensors, so how can feature extraction be set to account for all present and future use cases? Significant advances in inflow profiling have been achieved over the past few years, correlating DAS signals against flow-loop measurements and transient simulations. These technologies are very promising, especially now that wet connect and pumpdown technology for fiber optics is gaining more attention. Digital technology for orchestrating production-optimization and reservoir-management work flows has been increasingly embedding machine-learning functionality. Despite these advances, it remains a challenge to maintain these digital solutions over the long term. The most-successful companies in this area ensure the systems are tightly integrated with business-critical work flows, such as integrated activity planning, loss management, locked-in potential management, reservoir-surveillance planning, well-work-opportunity identification, production forecasting, and production back allocation. This involves a significant management of change process. It takes time and effort, but the rewards are worth it. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207879 - Expert Advisory System for Production Surveillance and Optimization Assisted by Artificial Intelligence by Carlos Mata, ADNOC Upstream, et al. SPE 201313 - Production Rate Measurement Optimization Using Test Separator and In-Well Sound Speed by Ö. Haldun Ünalmis, Weatherford SPE 203119 - Wireless Completion Monitoring and Flow Control: A Hybrid Solution for Extended Capabilities by Marcel Bouman, Emerson Automation Solutions

The reservoir characterization aims to provide the analysis and quantification of the injection-production relationship, which is the fundamental work for production management. The connectivity between injectors and producers is dominated by geological properties, especially permeability. However, the permeability parameters are very heterogenous in oil reservoirs, and expensive to collect by well logging. The commercial simulators enable to get accurate simulation but require sufficient geological properties and consume excessive computation resources. In contrast, the data-driven models (physical models and machine learning models) are developed on the observed dynamic data, such as the rate and pressure data of the injectors and producers, constructing the connectivity relationship and forecasting the productivity by a series of nonlinear mappings or the control of specific physical principles. While, due to the “black box” feature of machine learning approaches, and the constraints and assumptions of physical models, the data-driven methods often face the challenges of poor interpretability and generalizability and the limited application scopes. To solve these issues, integrating the physical principle of the waterflooding process (material balance equation) with an artificial neural network (ANN), a knowledge interaction neural network (KINN) is proposed. KINN consists of three transparent modules with explicit physical significance, and different modules are joined together via the material balance equation and work cooperatively to approximate the waterflooding process. In addition, a gate function is proposed to distinguish the dominant flowing channels from weak connecting ones by their sparsity, and thus the inter-well connectivity can be indicated directly by the model parameters. Combining the strong nonlinear mapping ability with the guidance of physical knowledge, the interpretability of KINN is fully enhanced, and the prediction accuracy on the well productivity is improved. The effectiveness of KINN is proved by comparing its performance with the canonical ANN, on the inter-well connectivity analysis and productivity forecast tasks of three synthetic reservoir experiments. Meanwhile, the robustness of KINN is revealed by the sensitivity analysis on measurement noises and wells shut-in cases.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.