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Static modeling workflow, integrating sequence stratigraphy, structural trend, and sedimentological/diagenetic process.
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The carbonate reservoirs lithofacies discussed in this paper contain heterogeneous pore types and properties. The challenge in predicting the distribution of the pores properties is through the technique used to construct a representative model to effectively describe the lithofacies distribution in 3D.
The studied reservoirs are part of the Lekhwa...
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... incorporation of recent seismic interpretations (horizons and faults), SCAL, core, log & well test data, production performance, and other available information is always done at the static modeling stage to reduce the reservoir uncertainties and improve the reliability of the reservoir model. Figure 6 shows innovative static modeling workflow that integrates stratigraphy, structural trend, and sedimentological/diagenetic process. It has been proven that this integrated methodology captured well the reservoir heterogeneity and provided a more robust dynamic model as described in reference 9 (Salahuddin, 2015). ...
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... utilized relationship and classification of Porosity -Permeability, Capillary Pressure (Pc) -Water Saturation (Sw) and Pore throat radius (PTR). The relationship between porosity and permeability for a defined RRT was established by the Pittman plot ( Figure 6, Figure 13a, Figure 15e, and Figure 16a). Well testing data in the reservoir interval was also incorporated to validate the water saturation of the studied reservoir. ...
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... utilized relationship and classification of Porosity -Permeability, Capillary Pressure (Pc) -Water Saturation (Sw) and Pore throat radius (PTR). The relationship between porosity and permeability for a defined RRT was established by the Pittman plot ( Figure 6, Figure 13a, Figure 15e, and Figure 16a). Well testing data in the reservoir interval was also incorporated to validate the water saturation of the studied reservoir. ...
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... modeling was built using effective porosity (PHIE) logs. Algorithm used for porosity distribution was Sequential Gaussian Simulation (SGS) conditioned to the pre-existing RRT distribution ( Figure 6). In the porosity modeling workflow, paleo-depth trend was applied as well. ...
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... this method, the permeability distribution was guided by a secondary property model (pre-existing porosity model) and was populated using porosity-permeability bivariate cloud transform which is unique for each rock type. By doing this, the permeability range will reproduce cloud variation as shown by logs and routine core analysis well data ( Figure 6, Figure 13a, Figure 15e, and Figure 16a). With porosity-permeability function alone, there is essentially no permeability variation for a particular porosity value in each rock type. ...
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... this method, the permeability distribution was guided by a secondary property model (pre-existing porosity model) and was populated using porosity-permeability bivariate cloud transform which is unique for each rock type. By doing this, the permeability range will reproduce cloud variation as shown by logs and routine core analysis well data ( Figure 6, Figure 13a, Figure 15e, and Figure 16a). With porosity-permeability function alone, there is essentially no permeability variation for a particular porosity value in each rock type. ...
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... height model with utilization of core and logs data was used to provide an accurate water saturation distribution at well scale as well as at reservoir model scale. In order to solve this issue then saturation height functions (modified Leverett J-function) were created for each of the identified RRTs (Figure 16). Well production testing data in reservoir interval is used to validate the water saturation of reservoir. ...
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The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field.
We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations.
In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model.
The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.
This paper presents a case study of the successful discovery of an appraisal well on a fault-bounded oil accumulation, carried out recently. The discovery was made adjacent to two big mature fields, highlighting the importance of careful geological and geophysical evaluation in identifying and targeting untapped potential hydrocarbon reserves. This paper also showcases the pre-drilled estimation of the probability of success (PoS) to properly evaluate the risk associated to the prospect and improve drilling success.
The study involved a comprehensive and thorough evaluation of geological, geophysical, and petrophysical data to understand the complexities of the petroleum system elements of the prospect. A range of advanced geoscience techniques were utilized, including seismic interpretation, advanced seismic imaging, well log analysis, core analysis, structural analysis, basin modeling, and play fairway analysis to develop a detailed understanding of the petroleum system elements of the area. This enabled the identification of high-quality potential hydrocarbon accumulation target in the fault-bounded reservoir. The appraisal well was drilled vertically using the latest drilling technologies and advanced data acquisition programs that allowed for better understanding on the reservoir characterization.
The well intersected significant oil column thickness with good reservoir properties and the oil was of good quality. The discovery of the fault-bounded oil accumulation was a significant achievement, highlighting the analysis of drilling data, mud log, well logs, and production testing data to further understand the reservoir potential and productivity for field development plan purposes. The discovery also demonstrated and provided valuable insights into the importance of careful geological and geophysical evaluation in identifying and targeting potential hydrocarbon traps and reserves with same geological setting and trap concept in other onshore areas.
The success of the appraisal well discovery on a fault-bounded oil accumulation demonstrates the importance of utilizing a range of advanced geoscience techniques to understand the complexities of the petroleum system elements of the prospect. This allows for the identification and targeting of potential hydrocarbon traps, and ultimately, the successful discovery of new reserves.
The paper is continuation of SPE-175682 and SPE-182963. This work illustrates horizontal well placement optimization studies conducted on a Cretaceous complex carbonate reservoir with thin oil column and strong water drive reservoir. A further complication for the well placement is the presence of some thin high permeability streaks intervals with permeability value of up to 1 Darcy. Early water breakthrough encountered in the existing oil producers is a serious problem which results in lower oil production rates, lower oil recovery, and increased lifting cost. In addition, premature water breakthrough would leave behind bypassed oil zones. Hence determining the optimum location of the wells is a critical and crucial decision to be made during a field development plan.
In this study, we apply integrated geosciences, geostatistical, and flow simulations to assess options for well placement. Base case porosity, permeability, and water saturation realization was selected from multi-realizations performed in the static model which was then used for sensitivity in fluid flow simulations. Flow simulation was used to analyze the performance of the well considering horizontal wells length, well azimuth, well inclination, wells position relative to the reservoir top as well as its position relative to the water contact. In addition, multi-scenarios of well placement relative to the high permeability intervals were created to see the impact on the oil rate, plateau, and water breakthrough time.
The flow simulation results show that the 4000 feet horizontal well that penetrating the upper high permeability streak gives the best performance in most of the cases. In contrast, the performance of the horizontal wells deteriorated rapidly once the well hit the lower high permeability streaks.
Some producers in the studied reservoirs have been drilled using the multidiscipline study recommendation. Actual property derived from the newly drilled wells displayed a very reasonable match to the expected property from model. In addition, production test and well commissioning result also showed comparable match with what was expected from dynamic simulation.
