For developing the Snorre North area which started its production on June 2001, various scenarios had been examined since its start of evaluation of a development plan of the Snorre South. After several screening processes, the following three alternatives were selected for detailed technical and economical evaluation. Case A: Subsea completion, tied-in to Snorre TLP (Tension leg platform). Case B: Processed and storage at FPSO (Floating production, storage and offloading). Case C: SSDPV (Semi-submergible drilling and production vessel) with drilling facilities. Processed oil is transferred to the Statfjord field via a new pipeline. When development plans are evaluated, it is very important to introduce estimated effects based on field experience and expected future technology development as well as detailed evaluation. The Snorre group had had experiences of operation on both a floating production system and a subsea completion system and tried to reflect these experiences into the concept selection. When impact on production profiles and reserves by changes in development solutions is evaluated, it is a normal procedure to carry out simulation studies with parameters defined for a specific solution and changes of reserves in different development scenarios are evaluated. Because of difficulties in defining parameters, however, there exist risks in the results of the simulation studies In concept selection for Snorre North development, the following four technical topics were evaluated; well length and complexity, WAG (Water Alternating Gas) feasibility, data acquisition and zonal isolation opportunities. Based on this evaluation, Case C (SSPV) provided the highest recoverable reserves and economics among the three alternatives and was selected for the development solution of the Snorre North area.
The classical deterministic approach subjectively treats the uncertainty range based on sensitivity analysis. The probabilistic approach is able to deliver quantified probability (distribution) for the possible outcomes of the process in question. This is a major advantage and allows the use of the results for the formal decision process. At the prospect evaluation stage, the expected reserves are usually estimated by a Monte Carlo analysis but the production profiles are somewhat problematic. In this paper, parametric production profile modeling is proposed to help the evaluation. The proposed approach is a correlative method. A representative data-set of historical reserves and production data for the producing fields of a basin are taken, and a series of four statistically acceptable correlations that will be subsequently used to predict the main parameters of a production forecast are developed. The four good correlations (reserves vs. peak flow rate, reserves vs. plateau production volume, reserves vs. decline rate and reserves vs. producing wells) are developed and the proposed method is proved to be a reasonable and justifiable one. At the production stage, a deterministic approach is commonly applied to evaluate the reserves. In this paper, recent probabilistic methods are introduced. Probabilistic methods available for uncertainty analysis range from full stochastic reservoir modeling, to very simplistic modeling and analytic propagation of errors. The full stochastic approach has been given up as a practical proposition due to the requirement of the large amount of manpower and computer resources. The dominating practice can be characterized as probabilistic post-processing of sensitivity data generated by reservoir simulation. A simplified reservoir model is constructed from such sensitivity data, e.g., by linear regression, typically to predict recovery (reserves) as a function of the uncertain input data. Next, this simplified reservoir model is used in a Monte Carlo analysis to produce a probability distribution for reserves.
When evaluating the reserves and generating the scenario for the development plan, it is necessary to select the epresentative values, i.e. area, thickness, porosity etc., for the specific reserves such as P10, P50 and P90 reserves. The easiest way to determine these values is to calculate the common percentile values that can generate the specific reserves. For instance, generating the reserves distribution multiplied by three (3) lognormal distributions of area, thickness and recovery factor, the P10 reserves can be generated by multiplying the common percentile values around P23 for each parameter distribution. However, this method does not account for the difference in the size of each variance. The percentile values are selected according to the specific percentile such as P23 in each parameter distribution, even though some variances of these distributions are small and others are large. Also, if the distributions are different from the lognormal and their types are mixed, in general case, it is difficult to derive the analytic solution of common percentile values, then an alternative search algorithm and tool is required. The approach presented here is the alternative way to select the representative values at the specific percentile of probabilistic reserves by direct usage of the actual trials in the Monte Carlo simulation. Although, for example, there are infinite combinations of values generating the P10 reserves, the distribution and combination of the values that hit the P10 reserves can be derived and evaluated by actual simulation trials. Monte Carlo simulation can show each parameter's distribution that hits the specific point of reserves. For area and thickness that have large variances in the exploration stage, the realized distributions are varied largely at each point, and these means or medians are changed clearly according to the varied points such as P10, P50 and P 90. On the other hand, for porosity or oil saturation that has a relatively small variance, the simulation shows nearly same distributions, and the difference in mean or median is also small at the different point of reserves, even though the specific point is changed largely. Adopting the proposed approach, the representative values can be easily derived accounting the variance and type of distribution for each parameter. This alternative method can provide more realistic values and scenarios through the process from the probabilistic reserves evaluation for the development planning.
Oil exploration and production industry faces more business risk than any other industry and therefore the risk management comes to be specially important for it. It may be helpful to recognize the kinds of the risk in this business and to understand how much which risk can be controlled by what kind of measure and which risk cannot be done so. At first it is important to make a quantitative analysis of the various risk in the E&P business. Risk management can be done as a risk control and a risk hedge. The typical risk control measure is the JNOC's financial support and risk hedge can be done in terms of currency risk hedge and oil price hedge. There are several ways of market risk hedge using financial derivatives such as SWAP, FLOOR and COLLAR. When we see the various kinds of risk in E&P business through its life span, we can understand the significance of the risk management.
&Teikoku Oil (Venezuela) Co., Ltd. started the oil rehabilitation project in Venezuela in 1992 under the first round of oil rehabilitation projects which Venezuela offered for foreign capital for the first time since 1976 when the Venezuelan oil industry was nationalized. One year later in 1993 we added one more project to our operations under the second round. The oil rehabilitation projects comprise the oil rehabilitation projects in a narrow sense and the exploration projects. As far as the unit cost of production concerns, the oil rehabilitation projects tend to have higher cost than the exploration projects due to the issue of operational efficiency. The unit cost of production in the whole of oil rehabilitation project is determined by the proportion of production from the oil rehabilitation and exploration projects to the total production. The stipulated unit cost of production under the contract with PDVSA minus the actual unit cost of production generates our profit. In the meantime, the investment to the oil rehabilitation projects is a loan to PDVSA, which loan generates receivable interests for us, therefore, our profit stems from the receivable interest as well as the above-mentioned spread of unit cost of production. Since 1992, we have experienced a lot of risks which undermined the above-mentioned profit-generating mechanism in the oil rehabilitation projects. The actual substance of the above-mentioned risks and measures taken to solve them will be discussed and investigated under this report. In particular, the risks on the foreign exchange, alteration of legal and taxation system, inflation, interpretation of contract, oil price, management of organization, friction with local community, safety & environment, and grasping the actual conditions in the block, are discussed and investigated under this report. After the discussion and investigation of the above-mentioned risks, the conclusion to relieve and diminish them is introduced.
The most important success key in waterflooding and other EOR processes is to properly inject fluids into a reservoir without channeling. The selective plugging technique by forming gel is one good approach where polymer with a crosslinker is injected into the reservoir. In this paper, CAG (CO2-Water Alternating Gelant Injection Process) is proposed as a new polymer gelation scheme for permeability profile modification. This method is comprised of alternate injection of the two solutions: carbon dioxide-saturated water and gelant (aqueous solution of polyacrylamide, sodium aluminate and sodium hydroxide). Theoretically, the two different slugs injected into the reservoir are mixed by viscous fingering and dispersion, and pH of the gelant can be lowered. It leads to generation of aluminum ions which crosslink the partially hydrolyzed polyacrylamide, and forming high viscous gel to plug the high permeable streak. To verify the efficacy of the CAG laboratory experiments were performed. The optimum concentrations of polyacrylamide, sodium aluminate and sodium hydroxide in gelant were selected to be 250mg/l from beaker tests. The experiments using glass-bead packed cores have revealed that the CAG could reduce the core permeability by about 1/60 to 1/360 of its original permeability. The slug size of gelant and CO2 water, gelant concentration, and core permeability were found to influence the behavior of gel formation. This method has only been verified by laboratory experiments, but is thought to be promising for future field application because of simplicity in theory and operation.
The Minami-Kanto Gas Field yields natural gas dissolved in brine. The reservoir consists of alternate thin layers of sandstone and mudstone. Production of the brine from the reservoir has caused surface subsidence. Part of the produced brine has been injected back underground to reduce the subsidence. In this study, we developed a K0-consolidation/swelling testing apparatus to simulate the deformation of the reservoir in a laboratory. Experiments with the apparatus were carried out on 6 mudstone core specimens from the reservoir. The results were as follows: 1) The specimens deformed plastically. 2) Some specimens continued to deform in spite of the static stress conditions. These observations suggest that the behavior can be regarded as viscous deformation. 3) The axial-strain behavior observed for the period of 72 hours could be fit to a rheological model. Calculation based on the model suggested that the viscous deformation would almost terminate in 30 days. 4) Correlation between the axial effective stress and the axial strain could be fit to an elast-plastic constitutive model.
Exploration for natural methane hydrate was carried out in the Nankai-Trough offshore Japan at a water depth of 945m over an 88 day period, from November 1999 to February 2000. This was a national project led by Ministry of International Trade and Industry (MITI) to seek a new energy source. It was organized by Japan National Oil Corporation (JNOC) in collaboration by Japan Petroleum Exploration Co., Ltd as the drilling operator. The Nankai-Trough well was drilled with R & B Falcon's deepwater semisubmersible, the “M. G. Hulme, Jr.”. The location was selected where BSR (Bottom Simulating Reflector) is the clearest on the seismic section. Six wells within 100m distances were drilled through the BSR horizon and the hydrate rich formation was confirmed between 1, 135m to 1, 213m BMSL (below mean sea level). This paper describes the planning, preparatory phase and the challenging operations.