Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Reserves Evaluation
Abstract A novel normalised plot technique is developed for reservoir characterisation and reserves estimation. This method is based on the Buckley-Leverett and Welge's fluid displacement theories. The theories suggest that under ideal conditions a normalised plot of oil recovery as a function of water or gas (displacing phase) throughput is independent of rate, drainage volume, and geometry of the system. This implies that in a homogeneous reservoir, well by well normalised recovery plots should collapse to one curve. In reality, well performance is influenced by reservoir heterogeneity, drive mechanism (bottom vs edge drive), gravity, and well locations. Therefore, the normalised curves do not often match and that leads to reservoir dynamic characterisation. The normalising factor, sweep volume, is related to the recoverable reserves (RR) that we seek to estimate. This paper presents the theoretical background of the technique and illustrates its application by presenting two field examples. Introduction Use of decline curve analysis for reserves estimation is a common practice in the petroleum industry. Reference 3 presents a summary discussion of the decline methods. However, these techniques are not based on any physical theories. Furthermore, decline analysis assumes constant operating conditions (choke size, artificial lift, etc), which is hardly ever met in field production operations. Experience has shown that analysis by exponential decline generally yields a conservative forecast (also known as a 'buyer's forecast'). On the other hand, hyperbolic extrapolation often results in an optimistic estimate ('vendor's forecast'). The applicability of the above techniques is basically a matter of convenience. The x-cut plot analysis, a second method, is based on fractional flow theory, but the assumption that a semi-log plot of permeability versus saturation data would be a straight line is not valid in all reservoirs and fluid flow systems. A third approach used in the industry is cumulative oil versus cumulative liquid (representing water influx in a water drive reservoir) production plot. Since drainage volumes and consequently recoverable reserves generally vary from well to well, such plots cannot be compared on a one to one basis. A meaningful comparison is achieved if the above conventional cumulative plots are normalised by their respective reservoir drained volumes (hence the name normalised plot). Mathematical Basis In 1942 Buckley and Leverett presented the basic equation for describing immiscible displacement in one dimension (also known as Frontal Advance Equation). In 1952 in another paper of fundamental importance, Welge enlarged upon Buckley and Leverett's work and derived an equation that relates the average displacing fluid saturation to that saturation at the producing end of the system. Welge also determined that pore volumes of cumulative injected fluid is equal to the inverse of the slope of the tangent on fractional flow curve. The above mathematical developments have become the basis of performance predictions of incompressible fluids in one- dimensional systems. In reality, reservoir fluids (particularly gas) are compressible and flow dynamics hardly occur in a linear system. Kern has shown application of the Buckley and Leverett equation to reservoir geometry to predict gas flooding performance. A generalised form (variable cross-section) of Frontal Advance displacement developed by Latil shows that the Buckley-Leverett theory is applicable in a reservoir as long as the displacement takes place in a porous medium whose geometry leads to the equipotentials and isosaturations being superimposed. The pore volume traversed by iso-saturation in a variable geometry reduces to a distance travelled in a linear system. P. 603
- Oceania > Australia > Northern Territory (0.47)
- Oceania > Australia > Queensland (0.29)
- Oceania > Australia > South Australia > Eromanga Basin (0.99)
- Oceania > Australia > Queensland > Eromanga Basin > Tintaburra Field (0.99)
- Oceania > Australia > Northern Territory > Eromanga Basin (0.99)
- (7 more...)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- (3 more...)
Abstract In the development planning studies of the Natuna gas project, a three-dimensional, two-phase reservoir simulation model of the Natuna gas field was used for evaluation of the gas reservoir and as a planning tool. A detailed sequence stratigraphic study of the gas field that incorporated seismic, well log and core data provided the basis for the geological model used in the reservoir simulation model. The objective of the development planning studies was to determine production well locations, production well drilling schedules, the total number of wells required, the timing for the installation of wellhead compression equipment and the gas production profile for the field as a function of time. The reservoir is estimated to be able to sustain a 2,400 million standard cubic feet per day (Mscf/d) methane production rate for 30 years or more. Introduction The Natuna gas field, located in Indonesian waters in the Natuna Sea, is estimated to have an original raw gas-in-place volume of over 200 trillion standard cubic feet (Tscf) making Natuna the largest undeveloped gas reserve in South East Asia. A three-dimensional, two-phase (gas and water) reservoir simulation model of the Natuna gas field was used for evaluation of the gas reservoir and as a basis for planning of the Natuna project. A detailed geological model of the carbonate formation provided the reservoir description used in the simulation model. The simulation model was used in development planning studies to determine production well locations, the well requirements versus time, total number of wells required, the timing for the installation of wellhead compression equipment and the gas production profile for the field as a function of time. Both initial field development and full-field development options were investigated with the reservoir simulation model. These studies are also the basis for the estimated ultimate recovery from the Natuna field. Field Overview The Natuna gas field lies approximately 140 miles northeast of Natuna Island and 218 miles northwest of Kalimantan (Fig. 1). Natuna Island is about 375 miles northeast of Singapore and 700 miles north of Jakarta. The water depth in the field area is approximately 475 feet. The discovery well, AL-1X, was drilled on the crest of the AL-Structure in 1973 by the Italian oil company AGIP and encountered approximately 5,250 feet of porous, gas-bearing carbonate section. After acquiring the concession for the area in 1980, Esso conducted a 2-D seismic survey over the D-Alpha Block and drilled four additional wells on the AL-Structure: wells L-2X, L-3X, L-4X, and L-SX (Fig. 2). The crest of the gas reservoir is at a depth of approximately 8,625 feet subsea. A gas-water contact was established by three wells (L-2X, L-3X, and L-SX) at a depth of 14,000 feet. The total volume of gas in the reservoir is estimated to be 222 Tscf. The composition of the gas is about 71% carbon dioxide, 28% methane plus heavier hydrocarbons, 0.5% hydrogen sulfide and 0.5 % nitrogen. The estimated gas recovery from the field is about 75% which would yield 46 Tscf of recoverable hydrocarbon gas. Reservoir Description The Natuna gas reservoir is interpreted to be an isolated, dome-shaped carbonate build-up structure (carbonate platform and reef complex) approximately 15 miles long and 9 miles wide. P. 567
- North America > United States (1.00)
- Asia > Indonesia > Natuna Sea (1.00)
- Asia > Indonesia > Jakarta > Jakarta (0.24)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (0.87)
- (2 more...)
Abstract Application of the reservoir compartmentalisation (RC) theory has increased reserves and opened new development and exploration opportunities in the Roma area. Reservoirs with pessimistic reserves assessments have been restudied using RC concepts, this resulted in reserve increases of 10% to 40%. These results encouraged workovers and tie-ins of old wells that were suspended for more than 10 years; some of these projects resulted with production and reserves better than expected. Another benefit of the RC theory is the sound explanation of unstable pressure trends. Pools assessed as small, because of rapid pressure decline during short testing, have been shut in for more than 10 years. During this time the pressure has slowly increased up to the initial reservoir pressure. The pressure support from an aquifer was believed to explain this behaviour. However, the RC model postulates that the depletion is the result of a small compartment intersected by the wells; and the pressure increase up to the initial reservoir pressure the result of gas coming from the adjacent compartments. In other words, the reservoir behaves as a small reservoir in the short term but as a large one in the long term. Finally, the understanding of the pressure behaviour of compartmentalised reservoirs have also increased the accuracy of production forecasts since it is possible to model a pressure decline for a particular arrangement of compartments. Case studies of two fields in the Roma area presented in this paper illustrate the importance of understanding compartments and being able to identify and prove significant additional reserves. In summary the RC theory has added value to the Roma area, not only by increased reserves but also reducing the risk of development and exploration prospects. Introduction The Roma Shelf is a hydrocarbon province located 500 km west-northwest of Brisbane, in the state of Queensland, Australia (Fig 1). The Roma area contains 42 fields and 80 pools (Fig 2). The first discovery happened by accident in 1900 when the Roma Council was drilling a water well and it blew out with a gas flow. Since then 402 wells have been drilled and 138 of these were successful. Currently, the Roma area is producing an average of 18 mmscfd and 20 BPD of condensate from 60 wells. The main producing horizon is the Precipice sand (Early Jurassic). The secondary horizons in order of importance are the Showgrounds sand (Middle Triassic), the Rewan (Late Permian), and the Tinowon sand (Early Permian). The total OGIP of the Roma fields amounts to 300 BCF and the total cumulative production, as of March 1996, is 198 BCF; that is 68% of the OGIP. The fields expected to recover another 26 BCF during the next 6 years which will give a total recoverable reserves of 224 BCF, or 75% of the OGIP. The growth of recoverable reserves during the last 5 years is illustrated in Fig 3. Field studies and recompletions were the main sources of reserves growth during this period. The field studies included the revaluation of reservoirs that produced better than expected, thus yielding an increase of recovery factor and OGIP. On the other hand, the recompletions included the perforations of zones that performed poorly during open hole DSTs, some of these zones resulted in improved flow rates and significant reserves. The contribution to reserves growth by development and exploration wells drilled during the same period were lower than the field studies and recompletions (Fig 4). One reason for these results, amongst others, is the maturity of the area, since most of the valid structures identified by the standard geological methods have been drilled. As a result, it is now difficult to find new structures with the same methodology in spite of the abundant data collected during the past 36 years of hydrocarbon exploration and development at Roma. The fact that several reservoirs are producing better than expected imply two things: first, that the current geological models do not fully explain the reservoir character. Second, that in the Roma area there are untapped reserves associated with the current pools that are yet to be found. The finding of these untapped reserves would probably not be possible with the current seismic alone since the structure and stratigraphy are beyond seismic resolution. Therefore, this additional reserves would more likely be found by the application of new theories and a combined effort of reservoir engineering and geology. P. 397
- Oceania > Australia > Queensland (1.00)
- North America > United States > Texas (1.00)
- Phanerozoic > Mesozoic > Triassic (0.54)
- Phanerozoic > Paleozoic > Permian (0.54)
- Oceania > Australia > Queensland > Surat-Bowen Basin > Avondale Field (0.99)
- Oceania > Australia > Queensland > Surat Basin > Raslie Field (0.99)
- Oceania > Australia > Queensland > Central Highlands > Bowen Basin (0.99)
- (5 more...)
Abstract Probabilistic estimation of hydrocarbon resource volumes requires the knowledge (or at least, the assumption) of the likely probability distributions of key reservoir and pool parameters used to estimate oil and gas reserves. Lognormal distributions are commonly associated with recoverable reserves, although distributions other than lognormal could describe the individual reservoir and pool parameters. This study was carried out in order to investigate the probability distributions of reservoir and pool parameters, to contribute to the better estimation of potential and actual reserves in the Cooper/Eromanga Basin, Australia. The key reservoir and pool parameters considered in this study were: pool area, net pay, porosity, hydrocarbon saturation and oil and gas recovery factors. All of the available data were derived from approximately 220 oil wells, 650 gas wells and over 130 fields in the Hutton, Toolachee and Patchawarra Formations of the Cooper/Eromanga Basin, in South Australia and Queensland. The raw data sets compiled were reviewed and re-sampled where necessary. Data points which were considered unrepresentative of the population distribution were discarded in the analysis. This primarily involved applying minimum value constraints to each data set. Below these minimum values, data were observed to be poorly or randomly represented. The resultant data above the applied minima are considered to represent technically and economically significant data sets. Frequency histograms were constructed for each of the key parameters and a range of distributions were fitted to each data set. The optimal or best-fitting distribution was computed for each parameter, in each formation. Results indicate that pool area, porosity, net pay and oil recovery factor are optimally or adequately modelled by lognormal distributions. Conversely, gas recovery factor and hydrocarbon saturation (which are both negatively skewed in the Cooper/Eromanga Basin), are better described by distributions other than lognormal. Along with the distributional analysis of each parameter, correlations between various parameter pairs were investigated by regression analysis, in order to test for independence. Results were not clearly defined within the entire data sets. However, significant correlations were revealed when smaller subsets of data from isolated areas of the basin were observed. The two most distinguishable were the negative correlation between porosity and depth and the positive correlation between hydrocarbon Saturation and porosity. Although, over the entire Cooper/Eromanga Basin, independence between the key parameters was assumed. The assumption allows all the parameters to be combined without the mathematical influence of covariance. Introduction There is always a significant level of uncertainty associated with modelling potential and discovered hydrocarbon reserves. The amount of oil or gas recovered from a single accumulation depends on various reservoir and pool parameters including: pool area, hydrocarbon saturation, net pay, porosity, and recovery factor. Estimation of potential hydrocarbon reserves requires the knowledge, or at least the assumption, of the probability distributions of these parameters. Any multiplicative process converges towards lognormality due to the central limit theorem. Furthermore, the product of a series of lognormal distributions is also lognormal (Capen). Potential reserves are calculated by the product of the above parameters and hence, are assumed to be lognormal. But what of the individual parameters? Are the parameters lognormally distributed themselves, or is it simply the multiplicative process that provides us with lognormal reserves? This paper addresses these issues. P. 631
- Oceania > Australia > South Australia (1.00)
- Oceania > Australia > Queensland (1.00)
- Research Report > New Finding (0.89)
- Research Report > Experimental Study (0.69)
- Oceania > Australia > South Australia > Eromanga Basin (0.99)
- Oceania > Australia > South Australia > Cooper Eromanga Basin (0.99)
- Oceania > Australia > Queensland > Eromanga Basin (0.99)
- (8 more...)
- Reservoir Description and Dynamics > Reserves Evaluation > Recovery factors (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation > Estimates of resource in place (1.00)
John S. Boardman, Resource Investment Strategy Consultants (RISC) Pty Ltd. SPE Member Abstract A practical business process is described which enables all categories of hydrocarbon resources to be valued in a commercially consistent manner. The benefits of the process are increased if there is a transparent corporate strategic plan which can be used to test the value of a resource against a desired portfolio, described quantitatively in terms of value and risk. Individual opportunities for both acquisition and disposal can then be measured against an assessed "Corporate preference profile" and pursued with increased confidence in the overall fit. Introduction Popular approaches to valuing recoverable hydrocarbons may expose investors to imprudent decisions due to lack of:Systematic capture of all components of value Systematic interpretation and measurement of risk parameters Clear understanding of Corporate constraints Measure of compatibility with a Corporate preference profile. The process described specifically addresses these issues by a systematic quantification of value and risk. This enables value to be added to the total business by acquisition and disposal of resources to maintain a resource base consistent with the requirements of a Corporate profile. The entire process is documented in a series of flowcharts which provide a guide to its execution at three levels of detail. Refer Fig A. (In the interests of space only a selection of these flowcharts have been included with this paper.) Specifically the value adding is achieved through a cycle of:–Acquiring resources with a risked value >expected exposure –Managing resources to achieve maximum retention value –Disposing of resources with a disposal value >risked retention value. P. 55
- Management > Risk Management and Decision-Making (1.00)
- Management > Asset and Portfolio Management (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (0.66)
Abstract The purpose of this study was to estimate the original-gas-in-place (OGIP) of a water-drive reservoir using optimization algorithm for Port Arthur field, Texas, U.S.A. The properties of the associate aquifer were also obtained. The good agreement, between the results from this study and those from simulation study, would be demonstrated in this paper. In this study, material balance equation for a gas reservoir and van Everdingen-Hurst model for an aquifer were solved simultaneously to calculate cumulative gas production. The result was then compared with cumulative gas production measured in the field that observed at each pressure. The following parameters were manually adjusted to obtain: OGIP, thickness of the aquifer, water encroachment angle, ratio of aquifer to reservoir radius, and aquifer's permeability. The procedure was then applied with simplex technique, an optimization algorithm, to adjust parameters automatically. When the difference between cumulative gas production calculated and observed was minimal, the parameters used in the model would be the results obtained. A water-drive gas reservoir, "C" sand gas reservoir in Port Arthur field, which had produced for about 12 years, was analyzed successfully. The results showed that the OGIP of 60.6 BCF estimated in this study was favorably compared with 56.2 BCF obtained by a numerical simulator in other study. In addition, the aquifer properties that were unavailable from the conventional plotting method can be estimated from this study. The estimated aquifer properties from this study were compared favorably with the core data. Introduction The original-gas-in-place of a water-drive gas reservoir was not easy to estimate by using analytical method because the complexity of water influx and aquifer properties involved. For a water-drive gas reservoir, the OGIP is an important parameter both for production performance and for feasibility study of converting a depleted gas reservoir to an underground storage. P. 325
- North America > United States > Texas > Jefferson County (0.70)
- Europe > United Kingdom > North Sea > Southern North Sea (0.47)
- North America > United States > Texas > East Gulf Coast Tertiary Basin > Port Arthur Field (0.99)
- North America > United States > Texas > East Gulf Coast Tertiary Basin > Port Acres Field (0.99)
- Europe > United Kingdom > North Sea > Southern North Sea > Southern Gas Basin > Sole Pit Basin > Block 53/02 > Arthur Field (0.99)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation > Estimates of resource in place (1.00)
SPE Member Abstract The Bokor field is located offshore Sarawak, Malaysia and is one of the largest fields in the Baram Delta Province (Figure 1). The A3/6 group of reservoirs is the largest amongst the Bokor reservoir groups. The reservoir comprises a series of multiple, stacked, well-developed, fluviomarine sandstones connected to a large aquifer. Production from this reservoir started in 1983 and since then some 13 MMstb of oil have been produced. To better understand the production performance, displacement mechanism and further development opportunities in this high viscous crude (10 cP) and strong water drive reservoir, a 3D sector reservoir simulation has been carried out. The model comprises 8640 active grid blocks, with 14 strings completed on four reservoir units with separate fluid contacts. The layering system and grid dimensions were found to be critical in the history matching process, which was supported by a X-sectional study carried out prior to embarking on the 3D model. Based on the history match, remaining oil was identified on the eastern flank, at the top of each sand unit (due to water under-running) and in the downdip area due to the existing ‘crestal oriented’ development. The history matched model was subsequently used to aid further development planning and to formulate a cost-effective reservoir management strategy. Various development scenarios were tested in this 3D model, which include infill drilling, horizontal wells and pressure maintenance by water injection. This paper describes the various steps taken to obtain a good history match over the 10 years of production history and discusses the findings of the prediction runs. Introduction The Bokor field is situated in the Baram Delta area 45 km offshore Sarawak, in a water depth of approximately 200 ft. The field is currently operated by PETRONAS Carigali, under a production sharing contract (PSC) between PETRONAS, PETRONAS Carigali and Sarawak Shell Berhad. The Bokor field, which commenced production in December 1982, has a Stock Tank Oil Initially In Place (STOIIP) of 775 MMstb. The A3/6 reservoir is the largest in the Bokor field containing some 198 MMstb. This represents about 26% of the Bokor total STOIIP. The reservoir currently contributes c.a 5% of the total Baram Delta production, while significant scope for further development still remains. Due to the importance of the A3/6 reservoir and also to aid further development planning, a 3D sector simulation study has been carried out with the following objectives; P. 541
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Malaysia Government (0.65)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation > Estimates of resource in place (1.00)
C.W. Arnold* and H. H. Djauhari* Abstract This paper reviews the first applications of horizontal wells in the Caltex Pacific Indonesia (CPI) area of operation in Central Sumatra, Indonesia. Drilled in late 1993 and early 1994, these wells now provide enough history for an initial evaluation of the results. These results are encouraging and CPI is reviewing further applications of this technology, which is viewed as another technique for squeezing more oil out of mature fields. The project histories, ranging from concept to evaluation, drilling, and actual performance results, are briefly covered. Emphasis is placed on the evaluation methodology, involving a blend of analytical and simulation methods, and comparison of performance predictions with actual results. Introduction CPI's area of operation is characterized by numerous mature fields with significant reserves, permeability, aquifer influx, and multiple stacked sandstone reservoirs. High producing water cuts and water coning are significant problems. Both the Minas and Petani fields used horizontal wells to target thick bottomwater sands which cone significantly in vertical wells. In Minas, the objective was to reduce water cut and capture bypassed oil from a fining upward section. In Petani, the concept was to reduce coning, increase rates, and improve well management by allowing the recompletion of existing vertical wells upward into unopened pay. The evaluations included a review of the geology and performance predictions designed to address the complications in each field. These include the very high permeabilities and fining upward sequence in Minas and the influence of neighboring wells in Petani. Generally, the results have been good and these projects offer useful case histories of horizontal well applications. Background Both the Minas and Petani fields are located in the Pertamina-CPI Production Sharing Contract Area in Central Sumatra, Indonesia (Fig. 1). The Minas field is the largest in Southeast Asia with an estimated original-oil-in-place (OOIP) of nine billion barrels. The Petani field, although smaller, is still sizeable with an estimated OOIP of about 690 million barrels. Petani is located 12 miles west of the Duri field, site of a large steamflood operation. Significant oil reserves remain in both fields and methods of efficiently recovering that oil are of strategic interest to the company. P. 337
- Asia > Indonesia > Sumatra > South Sumatra > South Sumatra Basin > Rokan Block > Rokan Block > Duri Field (0.99)
- Asia > Indonesia > Sumatra > Riau > Central Sumatra Basin > Rokan Block > Menggala Formation (0.99)
- Asia > Indonesia > Sumatra > Riau > Central Sumatra Basin > Rokan Block > Rokan Block > Minas Field (0.94)
- (6 more...)
- Well Drilling > Drilling Operations > Directional drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Conformance improvement (1.00)
Abstract The Dulang Field is located offshore east coast of Peninsular Malaysia in water depths of approximately 75 m. The field, discovered in 1981, is about 24 km by 3.5 km. After drilling 14 exploration/appraisal wells by both Carigali and its partner Esso Production Malaysia Inc., the central part of the field was developed as a unitized area in November 1990. Three 32-slot platforms have been installed in the unitized area, and development drilling is ongoing. Production commenced in March 1991 and is currently maintained at approximately 50,000 BOPD. The estimated OIIP (oil-initially-in-place) for the unitized area is in the order of 700 million barrels. There are 19 reservoir sands in Groups D and E which are of Middle-Late Miocene age. During the exploration/appraisal phase, oil and gas were encountered in the Group E and only gas in the Lower D1 reservoirs. Wireline formation pressure test data taken in the Lower D 1 reservoir in these wells plotted along a common trend with a gradient of 0.06 psi/ft. The lowermost gas pressure point was only 6 m above the normal hydrostatic gradient. It was therefore concluded that an oil column, even if present, would be thin. At the time, it was understandable that the gas pressures plotted along the same trend because the hydrocarbon column of the Lower D1 reservoir was large and extended beyond the limits of the major faults, suggesting a common pool. However, during the development drilling phase, it was discovered that the Lower D1 sandstone was a major oil reservoir, with estimated oil-in-place of about 100 million barrels. Oil columns of 75 m and 40 m have been proven up in the northern and southern flanks of the field, respectively, in the Lower D1. In addition, development plans were flexible enough to be able to effectively exploit the discovery. The well and formation pressure test data suggest that the Lower D 1 has a common pressure system in the gas cap over the central part of the field but different systems in the oil columns. Faulting is suspected to have provided both conduit and seal at different times to accommodate this phenomenon. P. 385
- Geology > Structural Geology (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.85)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.68)
- Geophysics > Borehole Geophysics (0.67)
- North America > Trinidad and Tobago > Trinidad > North Atlantic Ocean > Columbus Basin > TSP Block > Teak Field > Moruga Formation > Gros Morne Formation (0.99)
- Asia > Malaysia > Terengganu > South China Sea > Malay Basin > Block PM 6 > Dulang Field (0.99)
- Asia > Malaysia > South China Sea > Malay Basin (0.99)
- (2 more...)
SPE Member Abstract The Samarang field was discovered in 1972, offshore south west Sabah, East Malaysia. The field was declared commercial in 1974, and first production commenced in 1975. The M4/7/N1 cluster of reservoirs is one of the main groups of reservoirs in the Samarang field, containing some 23% of the field's expected ultimate recovery. The observed basal/edge natural water drive and the gascap expansion drive are expected to recover about 50% of the M4/7/N1's original oil in-place. However, the combined drive was not anticipated to fully sweep the reservoir. Hence, the need for a field wide review of the reservoir. To actively manage the remaining hydrocarbon resource a detailed 3D full field simulation project was initiated. A comprehensive geological study of the M4/7/N1 reservoirs was first conducted to formulate the geological input to the simulation model. The model was validated by history matching the reservoir's seventeen years of production data. This model was then used to actively manage the hydrocarbon resource base by: predicting future oil recovery under various development options, locating potential infill well locations, and by optimising the reservoir management strategy. Reservoir management of this mature field through the utilisation of reservoir simulation tools, has helped to maximise hydrocarbon recovery by determining the optimum depletion strategy with respect to the remaining hydrocarbon resource. Future infill drilling locations have been identified and the results from the model have been used to support the operator's development strategy for the pool. Introduction As operator for the Samarang Field, Sabah Shell Petroleum Company Limited (SSPC) initiated a reservoir review and simulation study on the M4/7/N1 reservoir. The main objectives of this work were to:–Develop a fully operational 3D full field model. –Confirm sweep efficiencies and ultimate recovery, and locate bypassed oil for further infill drilling. –Develop a reservoir management strategy to predict and optimise hydrocarbon recovery. The Samarang field is located offshore south west Sabah, East Malaysia, some 49 km north-west of Labuan island, in water depths of 30 to 160 ft. (Figure 1). It was discovered in 1972 by well SM-1, and was further appraised by three additional appraisal wells. The primary development started with first production in June 1975, and consisted of drilling 64 wells from the SMDP-A and SMDP-B platforms, and the SMJT-C, SMJT-D, and SMJT-E jackets (Figure 2). The initial development was completed in 1979. Additional developments took place in 1980 with installation of the SMJT-F and SMJT-G jackets. A revisit campaign was conducted between 1985 and 1987 consisting of eighteen new wells, twelve sidetracks, and eight re-completions. This was followed by a second revisit campaign conducted between mid 1991 and mid 1993; P. 261
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.47)
- Geology > Structural Geology > Fault > Dip-Slip Fault > Normal Fault (0.46)
- Asia > Malaysia > Sabah > South China Sea > Sabah Basin > Block SB301 > Samarang Field (0.99)
- Asia > Malaysia > Sarawak > South China Sea > Sarawak Basin > Baram Delta Province (0.98)