Main features of modeling terrigenous deposits in East-Siberia using the example of the Hamakinsky horizon

Background. The article considers the results of updating the geological model of the khamakinskii horizon reservoirs of the Chayandinskoe oid and gas field. The main aim is project the production of the oil rims and form a positive business case of the project. Materials and methods. Conceptual sedimentary model bases on the core of the 14 wells. Updating of the petrophysical model is the key to identify post-sedimentary transformations (like anhydritization and halitization) and the opportunity to correct the permeability trend. The tectonic pattern of the horizon based on the interpretation of 3D seismic data. There are two groups of faults were identified: certain and possible. Neural networks algorithm uses for a creating the predictive maps of anhydritization, which are used in the geological model. Results. Estuary sands influenced by fluvial and tidal processes dominate the khamakinskii horizon. The reservoir is irregular vertically: at the base of the horizon, there are sandstones of the delta front and there are alluvial valley with fluvial channels in the middle and upper parts. Eustary sands eroded by incised valleys (alluvial channels). According to the core and thin section analysis, the main uncertainty is sedimentary transformations of reservoir. It affects the net thickness and then the volume of oil in productive wells. 3D geological model includes the trends of anhydritization and halitization over the area, which makes it possible to obtain a more accurate production forecast. Conclusion. As part of the probability estimate of oil reserves, the main geological parameters that affect the volume of reserves were identified. Pilot project is planning to remove geological and technical uncertainties.

Long Term Production Strategy - Application of a Dynamically Integrated Reservoir and Production Model to Identify Compression Requirements and to Address Production Deferral in a Giant Gas Field

There are several operational challenges associated with a gas field producing in recycle or depletion mode, including a reasonable forecast and a robust production strategy planning. The complex reservoir dynamics further demands faster and reasonable analysis and decision-making. This paper discusses an all-inclusive integrated modeling approach to devise a production strategy incorporating the detailed compressor design requirements to ensure that a consistent production-stream is available in the long-term considering technical and economic aspects. The proposed production strategy is a two-fold approach. In the first step, the process utilizes the current reservoir simulation data in the production-forecast model. This history matched model captures the reservoir dynamics such as reservoir pressure decline and accounts for future wells drilling-requirements. However, the detailed production hydraulics in wellbore and surface facilities is not captured in the model. Further, to consider the declining well-performance and facility bottlenecks, integrated analysis is required. So, in the second step, the reservoir simulation model is dynamically integrated to take the input from the production model, encompassing detailed well and surface facility digital twins. The continuous interaction provides a highly reliable production profile that can be used to produce a production strategy of compressor design for the future. A strong interactive user-interface in the digital platform enables the user to configure various what-if scenarios efficiently, considering all anticipated future events and production conditions. The major output of the process was the accurate identification of the pressure-profile at multiple surface facility locations over the course of the production. Using the business-plan, field development strategy, production-profile, and the reservoir simulation output, reliable pressure-profiles were obtained, giving an indication of the declining pressures at gathering manifold over time. A well level production-profile-forecast helped in prioritizing wells for rerouting as well as workover requirements. As an outcome of this study, several manifolds were identified that are susceptible to high-pressure decline caused by declining reservoir pressures. To capture this pressure decline, a compressor mechanism was put in place to transfer the fluid to its delivery point. As this study utilizes several timesteps for the production forecast estimation, flexible routine options are also provided to the engineers to ensure that backpressure is minimized to avoid a larger back pressure on the wells for quick gains. This solution improves the efficiency of the previous approaches that were entirely relying on the reservoir simulation model to capture the pressure decline at the wellhead to forecast the compressor needs. In this methodology, the pressure profile at each node was captured to simulate a real production scenario. This holistic approach is in line with Operator's business plan strategy to identify the needs of external energy-source to avoid production-deferral.

Annual Work Plan Execution Performance Using an Automated Production Forecast Tool

Shaya Consortium ramped up its production from 60 KBOPD to almost 85 KBOPD as a result of an agile execution of its Field Development Plan, made of infill drilling, workover interventions, and full-field expansion of waterflooding. This combined activity made the planning process very complex and dynamic due to the high volume of operations and scenario evaluation. Additionally, the consortium was requested to provide a weekly production forecast to its major stakeholders highlighting all deviations from the original execution plan and remedial activities to come back on track. The proposed application tool has simplified and automated the forecasting processes using short-term updates of the executed activities from field reports, current well status, planned workover interventions, and new wells drilling schedule. Any deviation of the Annual Work Plan due to schedule variance or well performance is automatically adjusted by the tool, creating a new forecast to End-Of-Year or Quarter even Weekly, thus, reflecting the impact on the estimated recoverable volumes. The tool pulls information from different sources and consolidates them in a single unified environment, not only for forecasting but also as a visualization and analysis tool. Furthermore, it has several modules to facilitate the control of official type curves, scenario profiles for the Annual Work Plan, and it is fully linked to key corporate applications. This paper presents the development of a production forecasting tool that introduced a new way of working within the Shaya Production Team by improving activity scheduling and overcome underperforming new wells, keeping the operations team informed to facilitate the production management.

Digital-Twin for Production Monitoring and Optimisation: Two Case Study Application Examples

Well network simulation and optimization is an established technology within BP for production optimization. However, for simplicity, the processing facilities are usually only considered as fixed oil, gas and water flow rate constraints. Actual production limits vary as a function of operating conditions and/or cannot be measured directly (e.g. True Vapour Pressure (TVP) or gas velocity at the inlet separator nozzles). To improve on existing workflows, BP has expanded its existing petroleum engineering-focused toolkit and is now globally deploying an end-to-end production system digital twin that extends from the well choke to the facility export for system surveillance and optimization. The end-to-end production system digital twin is a cloud-based system that links sensor data from the asset historian with an equipment data model and third-party first principle steady state simulation tools for an accurate representation of the well network and processing facilities. It supports multi-discipline collaboration, particularly between Petroleum Engineers and Process Engineers, and is remotely accessible by a globally dispersed team. This integrated digital twin can be used in two modes: monitoring and what-if. In monitoring mode, the models are automatically updated hourly with real time data and key simulation results extracted and stored. These monitoring simulations generate virtual sensor output, providing insights that cannot be measured by real sensors. In what-if mode, engineers test scenarios risk-free to explore optimization opportunities. As well as routine optimizations to align with production forecast updates, this can also include scenarios during planned abnormal operations (e.g. facility equipment offline for maintenance or well flowback). An early pilot in a key production region delivered significant production upside and was foundational for the subsequent global roll-out program. This paper will illustrate two practical applications from early deployment activities: (1) condensate recovery optimization (2) well routing optimization / feasibility against variable processing facility limits.

Hybrid Data Driven Approach for Reservoir Production Forecast

Analytical models, in particular Decline Curve Analysis (DCA) are widely used in the oil and gas industry. However, they are often solely based on production data from the declining wells and do not leverage the other data available in the field e.g. petrophysics at well, completion length, distance to contacts... This paper describes a workflow to quickly build hybrid models for reservoir production forecast based on a mix of classic reservoir methods and machine learning algorithms. This workflow is composed of three main steps applied on a well by well basis. First, we build an object called forecaster which contains the subject matter knowledge. This forecaster can represent parametric functions trained on the well itself or more complex models that learn from a larger data set (production and petrophysics data, synthesis properties). Secondly this forecaster is tested on a subset of production history to qualify it. Finally, the full data set is used to forecast the production profile. It has been applied to all fluids (oil, water, gas, liquid) and revealed particularly useful for fields with large number of wells and long history, as an alternative to classical simulations when grid models are too complex or difficult to history match. Two use cases from conventional and unconventional fields will be presented in which this workflow helped quickly generate robust forecast for existing wells (declining or non-declining) and new wells. This workflow brings the technology, structure and measurability of Data Science to Reservoir Engineering. It enables the application of the state of the art data science methods to solve concrete reservoir engineering problems. In addition, forecast results can be confronted to historical data using what we call "Blind Testing" which allows a quantification of the forecast uncertainty and avoid biases. Finally, the automated workflow has been used to generate a range of possible realizations and allows the quantification the uncertainty associated with the models.

Petroleum Development Oman Forecasting Management System

Production performance forecasting is considered as one of the most challenging and time consuming tasks in petroleum engineering disciplines, it has important implications on decision-making, planning production and processing of facilities. In Petroleum Development Oman (PDO), which is the major petroleum company in Oman, production forecast provides a technical input basis for the economic decisions throughout the exploration and production lifecycle. Reservoir engineers spend more than 250 days per year to complete this process. PDO Forecast Management System (FMS) was introduced to transform the conventional forecasting of gas production. Employing the latest state-of-the-art technologies in the field of data management and machine learning (ML), PDO FMS aims at optimizing and automating the process of capturing, reporting, and predicting hydrocarbon production. This new system covers the full forecast processes including long and short-term forecasting for gas, condensate, and water production. As a pilot project, PDO FMS was deployed on a cluster of 272 wells and relied on agile project management approach to realize the benefits during the development phase. Deployment of the new system resulted in a significant reduction of the forecasting time, optimization of manpower and forecasting accuracy.

Intelligent Waterflood Optimization Advisory System – A Step Change Towards Digital Transformation

In this paper, we discuss the development of an end-to-end waterflood optimization solution that provides monitoring and surveillance dashboards with artificial intelligence (AI) and machine learning (ML) components to generate and assess insights into waterflood operational efficiency in an automated manner. The solution allows for fast screening of waterflood performance at diverse levels (reservoir, sector, pattern, well) enabling prompt identification of opportunities for immediate uptake into an opportunity management process and for evaluation in AI-driven production forecast solution and/or a reservoir simulator. The process starts with the integration of a wide range of production and reservoir engineering data types from multiple sources. Following this, a series of monitoring and surveillance dashboards of key units and elements of the entire waterflood operations are created. The workflows in these dashboards are framed with key waterflood reservoir and production engineering concepts in mind. The optimization opportunity insights are then extracted using automated traditional and AI/ML algorithms. The identified opportunities are consolidated in an optimization action list. This list is passed to an AI-driven production forecast solution and/or a reservoir simulator to assess the impact of each scenario. The system is designed to improve the business-time decision-making cycle, resulting in increased operational performance and lower waterflood operating costs by consolidating end-to-end optimization workflows in one platform. It incorporates both surface and subsurface aspects of the waterflood and provides a comprehensive understanding of waterflood operations from top-down field, reservoir, sector, pattern and well levels. Its AI/ML components facilitate understanding of producer-injector relationships, injector dynamic performance, underperformance of patterns in the sector as well as evaluating the impact of different optimization scenarios on incremental oil production. The data-driven production forecast component consists of several ML models and is tailored to assess their impact on oil production of different scenarios such as changes in voidage replacement ratio (VRR) in reservoir, sector, pattern and well levels. Opportunities are also converted into reservoir simulator compatible format in an automated manner to assess the impact of different scenarios using more rigorous numerical methods. The scenarios that yield the highest impact are passed to the field operations team for execution. The solution is expected to serve as a benchmark, upon successful implementation, for optimizing injection schemas in any field or reservoir. The novelty of the system lies in automating the insights generation process, in addition to integrating with an AI/ML production forecasting solution and/or a reservoir simulator to assess different optimization scenarios. It is an end-to-end solution for waterflood optimization because of the integration of various components that allow for the identification and assessment of opportunities all in one environment.

Case Studies of Digitalized Locate the Remaining Oil Workflows Powered by Hybrid Data & Physics Methods

Mature fields already account for about 70% of the hydrocarbon liquids produced globally. Since the average recovery factor for oil fields is 30 to 35%, there is substantial quantities of remaining oil at stake. Conventional simulation-based development planning approaches are well established, but their implementation on large, complex mature oil fields remains challenging given their resource, time, and cost intensity. In addition, increased attention towards reduce carbon emissions makes the case for alternative, computationally-light techniques, as part of a global digitalisation drive, leveraging modern analytics and machine learning methods. This work describes a modern digital workflow to identify and quantify by-passed oil targets. The workflow leverages an innovative hybrid physics-guided data-driven, which generates historical phase saturation maps, forecasts future fluid movements and locate infill opportunities. As deliverables, a fully probabilistic production forecast is obtained for each drilling location, as a function of the well type, its geometry, and position in the field. The new workflow can unlock remaining potential of mature fields in a shorter time-frame and generally very cost-effectively compared to the advanced dynamic reservoir modelling and history-match workflows. Over the last 5 years, this workflow has been applied to more than 30 mature oil fields in Europe, Africa, the Middle East, Asia, Australia, and New Zealand. Three case studies’ examples and application environments of applied digital workflow are described in this paper. This study demonstrates that it is now possible to deliver digitalized locating the remaining oil projects, capturing the full uncertainty ranges, including leveraging complex multi-vintage spatial 4D datasets, providing reliable non-simulation physics-compliant data-driven production forecasts within weeks.

FASAL concept in meeting the requirements of assessment and forecasting crop production affected by extreme weather events

The research in remote sensing application in India started first in agriculture way back in 1969. With the improvement in satellite sensors, data processing algorithms, models and computational power over time, this research culminated into development of operational projects of CAPE and FASAL, tackling an important issue of operationally providing pre-harvest crop production forecast to stakeholders. This review paper details the sequential developments in the use of remote sensing data for crop production forecasting. The scientific developments in the use of single and multi-temporal optical and microwave satellite images for crop identification and yield estimation in India have been reviewed. The case studies on use of remote sensing data for crop assessment under extreme weather events are also presented. These include the assessment of crop damage due to extreme weather events of floods, drought, and hailstorm. Examples on use of remote sensing for crop damage assessment due to pest and diseases and forecasting their incidence using satellite derived weather parameters are reviewed.