This paper explores the application of LCC (Life Cycle Costing) concepts in the oil and gas industry. The paper details research into the development of a LCC model for using in SAP (System, Application and Products in Data Processing). Information held in the existing system in the oil and gas industry has been investigated in order to determine whether or not it is adequate to support LCC application for assets. The conceptual framework will develop the LCC technique as a tool to carry out costing analysis for new and existing systems. It will ensure that existing systems data are optimized for use in LCC applications and will investigate the feasibility of integrating the LCC model with existing systems. Based on this conceptual framework a LCC model will be developed.
The proposed system model provides a structural breakdown of cost (SBC) that can be applied to any asset at any level, such as super system, system, sub-system and equipment level in its lifecycle. The purpose of this SBC is to act as an aide memoir, as the starting point for developing a project/asset specific SBC that is tailored to the needs of a particular LCC requirement. The overview of SBC is provided to identify the data requirement for estimated cost element and provides a definition for cost element.
Consequently, if SAP-LCC is used for analysis at every, it is possible to identify the level that is the most significant in order to develop or reduce the cost in LCC at that level.
Although application of horizontal well technology is categorized as a relatively new and expensive but it has been proven more effective and efficient in producing oil. However, the production rate strategy to obtain optimal result has not been well developed due to the difficulties to identify the influence and interaction of forces related to fluid flow mechanisms in reservoir. Particularly for oil reservoir with bottom water drive, the movement of water-oil contact/interface in a reservoir is strongly affected by interaction of the acting forces, such as: viscous force, gravity force and capillary force.
The main objective of this research is to study and investigate the influence of the forces interaction on production performance of the horizontal well producing oil from bottom water drive reservoir. For this purpose, a physical scaled model has been successfully constructed to simulate the production performance. Scaling down and construction of the model are performed by using dimensional analysis.
Results show that the interaction of the forces in the reservoir strongly affects the well production, in which the production performance increases as the ratio of gravity to viscous forces increases for all cases examined. Meanwhile, the changing of capillary force, which was believed by several researchers that it has no pronounced effect to the fluid flow mechanism in the reservoir, shows significant effect to the production performance of the well. The influence of reduced capillary forces in reservoir will enhance the well production performance. Consequently, in term of the ratio of gravity to capillary force, an increase in the ratio tends to improve the oil recovery.
Effective management of oil and gas fields over field life with the objective of maximising asset value can be a complex affair. Application and integration of various skills from subsurface (geology, geophysics, petrophysics, reservoir engineering, production technology and drilling) to surface engineering, production operations / surveillance and economics disciplines are required to manage technical, commercial and political challenges and uncertainties. In large organizations, there are the additional challenges of ensuring consistent work quality and process with multiple projects and technical personnel with different levels of skills and experience.
Subsurface and economic disciplines workflow over a field life from acquisition of exploration or development areas, exploration, development, production and abandonment work activities were mapped, improved and integrated to ensure work are executed more efficiently and correctly.
This paper describes the techniques and activities involved in developing this workflow which incorporates data and information management, technology mastery and promoting a collaborative and integrated multidiscipline approach. It was developed and deployed in two phases over 3 years and became the first web enabled workflow in the organisation. Pilot implementation was carried out on a number of domestic, international and outsource Field Development and Field Review projects.
Case histories of the application, benefits gained and lessons learnt are discussed this paper.
Information about spatial distribution of rock true resistivity (Rt) throughout an oil or gas field is always desired. Since seismic survey is the only widespread source that provides information for inter-well locations, considerable efforts have been devoted to extract as much data as possible from it. It is therefore desirable to be able to extract R t from seismic attributes normally represented by acoustic impedance (AI). This paper presents a field trial of an approach that is basically an application of artificial intelligence (artificial neural network, ANN) on well-log and seismic data based on a proven theoretical relationship that relates R t to AI. The approach itself, which has been successfully verified through a series of laboratory trials, includes training of the ANN using relevant well-log data, Rt prediction using the trained ANN, and blind tests as means of result validation. An oil field located in East Java is chosen for the trial. It has been shown that there is a certain correlation between log-derived resistivity and log-derived AI . As the approach is applied to map the resistivity and water-saturation, comparisons between conventional/ deterministic water-saturation (Sw) map and the corresponding map resulted from the trial has shown the superiority of the method in presenting inter-well variations in water-saturation. It is also found that the new method has provided a high level of flexibility in interpreting and distributing the inter-well Sw values.
Kramer, Darrell (International Reservoir Technologies, Inc.) | List, David (International Reservoir Technologies, Inc.) | Tillman, Laura (International Reservoir Technologies, Inc.) | Jagiello, Keith (International Reservoir Technologies, Inc.) | Gould, Thomas (International Reservoir Technologies, Inc.)
Since 1996, International Reservoir Technologies (IRT) and Petroleos de Venezuela, SA (PDVSA) have completed four (4) integrated studies within Block V Lamar, Block VI Lamar, Block V Centro, and Centro Sur Lago Areas of Lake Maracaibo, Venezuela. These studies have identified four major and numerous minor "positive flower" structures along confining bends of three major strike slip faults. These flower features provide the structural trap that has accounted for some 623 MMstb of oil production from the Eocene Misoa B and C sands.
Positive flower structures are often dismissed as non-existent and are often interpreted as reactivated normal faults with reverse movement. Although the normal displacement is true on the examples shown in this paper, we will show several examples of bounding arcuate reverse faults that coalesce into a single fault at depth commonly referred to as "Positive Flower" structures.
3D seismic, in conjunction with dipmeters, logs, fluid contacts, engineering tests and production data provided the necessary information to build a detailed, comprehensive and consistent 3D geologic model of these structures. The integration and use of all possible data sources was key to accurately mapping the associated reservoirs.
The studies have led to improved knowledge of the timing and development of flower structures with respect to hydrocarbon migration and trapping. Production continues in these structures and development drilling continues both within the flower structures and to drain oil that has been trapped on the underside of bounding reverse faults. The integrated study approach has lead to optimized development of these complex "Positive Flower" structures, but is also applicable to other structural and stratigraphic regimes.
Flew, Stephen (Schlumberger) | Mulcahy, Matthew (Schlumberger) | Stelzer, Hermann (Schlumberger) | Boitel, Alain (Schlumberger) | Zainuddin, Faizal (Petronas Carigali Sdn Bhd) | Harun, Abd Rahman (Petronas Carigali Sdn Bhd) | Hassan, Zulkarnain (Petronas Carigali Sdn Bhd) | Aziz, Kamaroll Zaman A. (Petronas Carigali Sdn Bhd)
After more than 20 years of production, the 800 million STB Bokor field, offshore Sarawak, Malaysia, is set to undergo a revitalization to increase production rates and recovery factors. A joint PETRONAS Carigali (PCSB)-Schlumberger team was formed to review the field and develop the first full field simulation model, which will be utilized as a ‘live' model for identifying further potential and for future reviews. This paper outlines the modeling workflow and processes developed to allow the study to be completed within a shorter duration than with a conventional approach, as well as the key study results.
Made up of some 130 vertically stacked reservoirs over a 6,000-ft interval, evaluation of the field is hampered by the lack of any useful seismic images over the hydrocarbon zone, owing to shallow gas and other anomalies. Understanding the reservoir behaviour has always been a challenge, with the degree of sand consolidation in the reservoirs varying from totally unconsolidated at 1,500 ft subsea, filled with 10 cP oil, to consolidated deeper sands, with 0.1 cP oil at 7,500 ft subsea. In addition, the limited pressure depletion owing to the presence of a very strong aquifer, and the fact that many fluid contacts have not been penetrated, means that significant uncertainty still exists over the likely stock-tank oil initially in place (STOIIP).
By fully utilising all available data and an iterative team-based approach to history-matching the geostatistical models with production data, an understanding of the key parameters was gained. This revealed that in many reservoirs, what was previously considered poor-quality rock was in fact a major contributor to reservoir flow, being neither as poor in permeability nor as high in water saturation as previous interpretations suggested.
Through the increased understanding gained during the review, robust predictions have allowed new facilities to be appropriately sized, allowing the field to begin its new lease of life.
The paper introduces some problems that subdivision injection and production technology is facing in the stage of extra-high water cut production in Daqing Oilfield, the problems have become main factors affecting sustained development of Daqing Oilfield, like subdivision water injection, separate layer fracturing in thin restraining barrier and injecting surfactant to reduce pressure problem, each of them is close to challenge technical limit.
The last decade has witnessed an almost universal shift of emphasis away from operator functional objectives towards the actual business performance of individual assets, which in turn is highly dependent on the day to day production environment and its evolution. Improvements in production data acquisition and data management technology are not always translated into real and quantifiable productivity gains, due to constraints in manpower, tools and expertise. This paper describes a methodology which can be used to address these issues through an integration of software technology and improved work processes.
A system has been developed which links on-line (real-time and near real-time) downhole, surface and corporate data to ensure that reservoir, well and facilities models are constantly monitored and updated to reflect actual operating conditions. With this "always current" encapsulation of the production system (between reservoir and delivery point) it is possible to perform a wide range of tasks to manage the asset performance: production optimization, facilities design and planning, production forecasting, allocation and loss management, etc.
The system integrates a number of existing software applications, and also streamlines the number of complex engineering tasks which are normally required. The system has been applied in a number of actual operational environments and the results of some of these applications are presented in this paper.
The main technical contributions described in this paper include:
A robust optimization technology which has been technically proven in a large number of field applications.
A workflow solution which automates production data transfer, manipulation and calibration to provide constantly updated well, facilities and network models.
Integration of the system with SCADA/DCS and corporate databases to shorten the field optimization cycle from months to hours.
Use of the technology for loss management, production forecasting and advanced surveillance.
The major problem that concerns log analysts is their interpretation forshaly formations. The previous models that were used to estimate thehydrocarbon saturation in shaly formation revealed a wide area ofdiscrepancies. Shaly sand models based on shale volume fraction(Vsh) fail to consistently predict representative values ofhydrocarbon saturation from wire line data. Cation exchange capacity-basedmodels require laboratory-determined concentration of sodium exchange cationassociated with clay (Qv) value which is not commonly available tothe log analyst. In addition, different laboratory techniques are found toyield different Qv values for the same core sample. Moreover,Louisiana State University (LSU ) model is very complex in their calculationand depends on the determination of Qv by using a complicatedtechnique. Also, the theoretical model such as Berg's model, use a repeatedcomplicated iteration technique. Therefore, a new shaly model is presentednamed ANN's model. It is built a non-linear model to predict watersaturation as a function of conventional well logs. The neural network wastrained and tested over a wide range of Bahariya formation, which characterizedby high clay content. The resulting models provide an excellent correlationbetween the inputs and outputs. The accuracy of results derived from theANN's model is highly exceeds that of the previously published shalysand models. In general, an ANN's model displayed a correlation coefficient of0.9968 where 1.0 is a perfect match. Moreover, maximum absolute error of lessthan two percent were observed.
This study addresses a few issues regarding the experimental design and analysis (EDA) methods, and its applications in multiple deterministic modelling to quantify hydrocarbon in-place (HCIIP) probability distribution curves. They include (1) the suitability of using Latin Hypercube design (more than 3 levels in the input variables) compared with the 3-level fractional factorial designs (FFD) (e.g. 2k-p FFD with centre & axial runs); (2) multidimensional kriging versus response surface method as the analysis technique to fit surrogate equation models; (3) the selection of test runs to evaluate the fitted surrogate equation models; (4) sensitivity of the assigned input probability values on the output response distribution curve; (5) the confidence interval of the cumulative probability distribution curve at median (P50) or other percentiles (e.g. P10 & P90); and (6) how to select representative 3D geological models at P10, P50 and P90.
In this study, an Excel spreadsheet, which mimics the 3D geological modelling workflow to estimate the oil in-place volume, is used to compare the above mentioned designs and analysis techniques. Some guidelines to select representative test runs/models to evaluate the fitted surrogate equation models are discussed. In multiple deterministic modelling, all the input uncertainties will normally be quantified with an uncertainty range from low, base to high values, and with their assigned probability values. Then, a decision tree can be built to visualise the full range of all the possible 3D geological models, and their respective probability of occurrence can be calculated from the probability of the input uncertainties. Combining the probability and the predicted responses from the evaluated surrogate equation model, the output response (e.g. oil in-place) probability distribution curve can then be built. Sensitivity of the input probability values on the output response distribution curve is briefly discussed. There will be some errors associated with the predictions from the surrogate equation models, and therefore confidence interval must be generated to account for them. With the P10, P50 and P90 output response values, the confidence intervals and the probability of occurrence, some representative 3D geological models at P10, P50 and P90 can be selected.
A 3D multiple deterministic modelling case study is then used to demonstrate the methodology in estimating the oil in-place distribution curve of a synthetic reservoir that is adopted from an actual fluvial reservoir.
In conclusions, the EDA methods can be used to minimise the number of 3D geological models that must be generated to build good HCIIP probability distribution curves. By using bootstrapping and normal confidence interval, fitting errors can be quantified and representative 3D geological models for P10, P50 and P90 values can be selected accordingly.