Babs Oyeneyin

Situation awareness in context-aware case-based decision support

Humans naturally reuse recalled knowledge to solve problems and this includes understanding the context i.e. the information that identifies or characterizes these problems. For problems in complex and dynamic environments, providing effective solutions by operators requires their understanding of the situation of the environment together with the context. Context-aware case-based reasoning (CBR) applications uses the context of users to provide solutions to problems. The combination of a context-aware CBR with general domain knowledge has been shown to improve similarity assessment, solving domain specific problems and problems of uncertain knowledge. Whilst these CBR approaches in context awareness address problems of incomplete data and domain specific problems, future problems that are situation-dependent cannot be anticipated due to lack of the facility to predict the state of the environment. This paper builds on prior work to present an approach that combines situation awareness, context awareness, case-based reasoning, and general domain knowledge in a decision support system. In combining these concepts the architecture of this system provides the capability to handle uncertain knowledge and predict the state of the environment in order to solve specific domain problems. The paper evaluates the concepts through a trial implementation in the flow assurance control domain to predict the formation of hydrate in sub-sea oil and gas pipelines. The results show a clear improvement in both similarity assessment and problem solving prediction.

Heavy Oil Recovery: A Cold Process Using CO2-EOR Technique

Owing to substantial improvement in enhanced oil recovery (EOR) technologies and significant decline in discovery of light and medium crude oil fields, the heavy oil development is progressively receiving considerable attention to fill the supply gap. Cold heavy oil production (CHOP) using captured carbon dioxide (CO2)-EOR technique was investigated using the state-of-the-art Integrated Product Modelling packages of Petroleum Experts as part of the Well Engineering Research Group unconventional oil reservoir management studies being undertaken at Robert Gordon University. Beyond ascertaining the feasibility of the CHOP using CO2-EOR, the objectives of the investigation were to establish the process requirements at the onshore facilities based on series of parametric studies and to enhance the understanding of the subsea integrated injection and production systems during the injection process. The injection system consisted of an injection well connected to a 240 km subsea pipeline transporting CO2 from an onshore compression station. The production system included a topside separator connected to the production well via a 2km riser. A broad range of reservoir production history was used and the simulation results indicate that heavy oil displacement was easily achievable under miscible conditions (i.e. high reservoir pressure), but the production trend was strongly influenced by the reservoir characteristics (i.e. GOR, WC, Pressure).

Rheological Characterisation of Heavy Oil and Impact on its Production Enhancement

Criticality of rheology for heavy oil recovery is the main purpose of this paper supported by different results. The Bingham Plastic, Power Law and Herschel Bulkley rheological models have been adopted for the purpose of this paper. Rheological characterisation was carried out for different temperatures. Rheological behaviour of non-Newtonian heavy oil for different shear rates is analysed in this paper. Effective shear and bulk viscosities for different flow rates are compared for all rheological models. Using the horizontal well productivity model, the drawdown values for all rheological models are determined. Similarly for the sand management purpose the critical rates of Newtonian and these three non-Newtonian fluids are plotted to determine the critical drawdown values for each type of fluid. Impact of drainage profile on the effective viscosities is also compared for different drainage profiles. Shear rate models are proposed in this paper for Bingham Plastic, Power Law and Herschel Bulkley rheological models. The new Micro-PVT equipment is also introduced for determining the PVT properties and rheological behaviour of heavy oil. Nomenclature

UCS Neural Network Model for Real Time Sand Prediction

Exploration and production activities have moved into more challenging deep-water and subsea environments. Many of the clastic reservoirs in these environments are characterized by thick overburden, HP-HT and largely unconsolidated formations with challenging sand management issues. For effective overall field/reservoir management, it is crucial to know if and when sand would fail and be ultimately produced. Field-life sanding potential evaluation and analysis, which seeks to evaluate the sanding potential of reservoir formations during the appraisal stage and all through the development to the abandonment stage, is therefore necessary so that important reservoir/field management decisions regarding sand control deployment can be made. Recent work has identified Unconfined Compressive Strength (UCS) as a key parameter required for the evaluation and analysis of sanding potential of any reservoir formation. There is therefore the need to be able to predict this important sanding potential parameter accurately and in real time to reduce the level of uncertainties usually associated with sanding potential evaluation and analysis. In this work, neural network coded in C++ was trained with log-derived petrophysical, geomechanical and textural data to develop a stand-alone model for predicting UCS. Real-time functionality of this model is guaranteed by real time data gathering via logging while drilling (LWD) and other measurement while drilling (MWD) tools. The choice of neural network over and above other methods and techniques which have been widely used in the industry was informed by its ability to better resolve the widely known complex relationship between petrophysical, textural and geomechanical strength parameters.

Investigation of CO2 Sequestration during Cold Heavy Oil Production

CO2 sequestration during cold heavy oil production using captured carbon dioxide was investigated using REVEAL of Petroleum Experts. The results indicated that the CO2 release was influenced by the production phases. The prediction showed high CO2 retention in the first few years post start-up, followed by a gradual decline toward 16.5% post peak production. The recovery rate was strongly influenced by the reservoir characteristics, such as fluid properties, permeability, aquifer, and well completion. Horizontal wells provided better performance than vertical wells. The CO2 utilization and retention per barrel of heavy oil increased as the CO2 injection pressure increased.

The Prospect of Deepwater Heavy Oil Production Using CO2-EOR

The prospect of unconventional oil development has long been coming to offset the rapid decline of conventional crude. And looking ahead, the worry is already turning away from the onshore exploitation to the challenging offshore environment, with the question being whether the emerging technology can overcome the challenges of deep-water heavy oil production. In economics terms, the immiscible process shows a negative return, a longer payback time, and a low net present value. With an increased revenue through increased production, there is a degree of strong, dynamic, and appealing prospect to any future heavy oil development using miscible process.

Chapter 8 - Risk Assessment Criteria for Effective Sand Management☆

Abstract The major challenges for asset teams in the development of oil/gas fields in deepwater environments, especially in relation to total sand management, are in how to provide inputs into the field/subsea and topsides design in terms of possible quantity and particle size distribution of sand and frequency of sand produced and transported through the wellbore into the subsea and topside facilities as well as how to optimise a well design that is fit-for-purpose and to maximise the individual well’s production and field development performance. This includes effectively managing the sand and multiphase fluid production as well as the integrity of the facilities. This chapter presents a step-by-step total sand management solution strategy that can enable managers of subsea installations and complex fields to improve intervention and production efficiency through continuous process optimisation, reducing non-productive time and also guaranteeing flow assurance that can reduce lifting costs per barrel.

Sand Control Completion Strategy

Abstract Sanding in a well/reservoir is considered a life-cycle problem that changes over time due to constraints imposed by operational factors of exploration, drilling, production, and stimulation. Predictions made prior to drilling a well cannot therefore be said to be valid during or after drilling and during production operations. Establishment of a sand management strategy is a logical follow-up to sand production rate prediction. Real-time prediction of sanding potential holds a lot of promise for the industry, especially in relation to the economically wise decision regarding sand control and type of sand control to be adopted, and will become easy for any reservoir management teams. This chapter focuses on establishing an appropriate sand management philosophy that limits or excludes sand production, which includes two major categories of sand control: passive sand control involving production rate limitation below the critical sand production rate, and active sand control including sandface downhole control, remedial sand control, and surface sand control with solids control facilities.

Fundamentals of Petrophysics and Geomechanical Aspects of Sand Production Forecast

Abstract Sand production is one of the major problems facing the oil and gas industry and is common to clastic sedimentary basins throughout the world, affecting thousands of oil and gas fields. Sand production is not only a safety hazard for the oil industry but is also a source of revenue loss. This chapter focuses on sand prediction, which is a term that refers to all the processes leading to the effective quantification of the propensity of a failed reservoir to produce sand. Sand management, and the decision whether to exclude or accept sand production, requires an understanding of the mechanisms that cause sanding and the development of a field-validated methodology to predict the critical conditions for sand production. Any sand prediction estimation starts with an evaluation of the time to rock failure.

Introduction to Sand and Condition Monitoring Strategies for Asset Integrity

Abstract Sand detection is an important part of work in the oil and gas industry. It may be achieved in a number of ways including, at the extreme, the unplanned shutdown of a production separator due to high levels of deposited sand (operability problems) to other methods such as the sampling of well-stream fluids. This chapter focuses on the use of sand detectors (sensors) and monitors that provide early warning of sand production. Sand sampling and measurement of sand accumulation in vessels is also covered. The three main methods for detecting/monitoring sand (and covered in this chapter) include on-line non-intrusive: ultrasonic, acoustic sensors; on-line intrusive: material loss from a probe or element, or possibly the pipe wall; and sand sampling from flowlines, pipelines, and process vessels.