Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Trombin, Gianluca (Eni S.p.A) | Vinci, Fabio Francesco (Eni S.p.A) | Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD) | Bazzana, Michele (Eni Iraq BV) | Viviani, Enea (Eni Iraq BV)
The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes.
In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells.
In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%.
The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
This paper presents a diagnostic workflow to understand and implement rock and fluid modeling in a diagenetically heterogeneous and hydrodynamically pressured Middle East carbonate field. The workflow allows interactive field data integration, provides guidance for reservoir property distribution and fluid contact generation in order to improve reserves and forecasting estimation. The workflow is useful to a reservoir modeler in QA/QC role and in this case it proves particularly applicable in an organization with constrained resources during the farm-in process. The workflow runs on numerical methods within the static model to avoid database discrepancy during the diagnostic process. Using the core (CCAL, SCAL), log and pressure database, the geoscientist can assess subsurface modeling outputs from the simplest to more complex deterministic scenarios. The process aims to minimize the discrepancy between data input and model output while continuously honoring the data, maintaining realistic correlations (e.g. between static permeability and water saturation) and respecting inherent uncertainty.
Using a data-rich Middle East carbonate reservoir, the pre- and post-diagnostic comparison of 3D modeled reservoir properties to the input data are demonstrated. Diagnostic steps have helped to understand potential subsurface scenarios and thus minimize the discrepancy post exercise. The value of the workflow is its ability to pinpoint the key uncertainties in rock and fluid modeling from the field’s vast dataset in a shorter diagnostic time. The application of the workflow in this carbonate reservoir case study increases the importance of geological and property driven rock type classification and its 3D distribution in matching the water saturation profile. This proved particularly challenging in this case study due to the field’s compartmentalization - fluid contact scenario.
This study examines which is the margin of usability for Artificial Intelligence (AI) algorithms related to the rock properties distribution in static modeling. This novel method shows a forward modeling approach using neural networks and genetic algorithms to optimize correlation patterns among seismic traces of stack volumes and well rock properties. Once a set of nonlinear functions is optimized in the well locations, to correlate seismic traces and rock properties, spatial response is estimated using the seismic volume. This seismic characterization process is directly dependent on the error minimization during the structural seismic interpretation process, as well as, honoring the structural complexity while modeling. Previous points are key elements to obtain an adequate correlation between well data and seismic traces. The joint mechanism of neural networks and genetic algorithms globally optimize the nonlinear functions and its parameters to minimize the cost function. Estimated objective function correlates well rock properties with seismic stack data. This mechanism is applied to real data, within a high structural complexity and several wells. As an output, calibrated petrophysical time volumes in the interval of interest are obtained. Properties are used initially to generate a geological facies model. Subsequently, facies and seismic properties are used for the three-dimensional distribution of petrophysical properties such as: rock type, porosity, clay volume and permeability. Therefore, artificial intelligence algorithms can be widely exploited for uncertainty reduction within the rock property spatial estimation.
Pavlov, Dmitry (Sakhalin Energy Investment Company Ltd.) | Fedorov, Nikolay (Sakhalin Energy Investment Company Ltd.) | Timofeeva, Olga (Sakhalin Energy Investment Company Ltd.) | Vasiliev, Anton (Sakhalin Energy Investment Company Ltd.)
This paper summarizes the results of 3 years collaborative efforts of the Geophysicist, Production Geologist and Reservoir Engineers from the Astokh Development Team and a Geochemist from the LNG plant laboratory on integration of reservoir surveillance and reservoir modelling.
In period 2015 – 2018 a large bulk of geological and field development data was collected in Astokh field, in particular: cased and open hole logs, core, open hole pressure measurements, flowing and closed-in bottom hole pressures, well test data, new 4D seismic surveys (2015, 2018), fluid samples. Since 2016, essential progress was made in oil fingerprinting for oil production allocation in Astokh field. Simultaneously, the need for update of static and dynamic models was matured upon gaining experience in dynamic model history matching to field operational data (rates, pressures, well intervention results). In other words, the need in update of geological architecture of the Astokh reservoir model was matured upon reaching critical mass of new data and experience. To revise well correlation, it was decided to combine different sorts of data, in particular seismic, well logs and core data and reservoir pressures. Different pressure regimes were identified for 3 layers within XXI reservoir. Pressure transient surveys were used for identification of geological boundaries where it's possible and this data was also incorporated into the model. Oil fingerprinting data was used for identification of different layers and compartments. Integration of pressure and oil geochemistry data allowed to identify inter-reservoir cross-flows caused by pressure differential. Based on all collected data, sedimentology model and reservoir correlation were updated based on sequential stratigraphy. As a result, a new static model of main Astokh reservoirs was built, incorporating clinoform architecture for layers XXI-1' and XXI-2. To check a new concept of geological architecture material balance model was used and matched to field data
Integration of geological and field operational data provided a key to more advanced reservoir management and development strategy optimization. Based on updated reservoir model, new potential drilling targets were identified. Also, with new well correlation, water flood optimization via management of voidage replacement ratio was proposed. The completed work suggests essential improvement in reservoir modelling process by inclusion of various well and reservoir surveillance data.
The paper consists of the following sections: Introduction Field geology Field development history Scope of work complete and main results Proposed well correlation update for XXI-1' and XXI-2 layers Integration of well logs, pressure and fluid analysis data Connectivity between layers XXI-S, XXI-1' and XXI-2 Integration of pressure and oil fingerprinting data Connectivity within layers XXI-S, XXI-1' and XXI-2 Results of pressure interference tests Testing of new well correlation concept in material balance model Proposed reservoir correlation updated based on seismic data New geological concept New depositional model Integration of core data Changes in reservoir architecture Conclusion Main results and impact on field development
Field development history
Scope of work complete and main results
Proposed well correlation update for XXI-1' and XXI-2 layers
Integration of well logs, pressure and fluid analysis data
Connectivity between layers XXI-S, XXI-1' and XXI-2
Integration of pressure and oil fingerprinting data
Connectivity within layers XXI-S, XXI-1' and XXI-2
Results of pressure interference tests
Testing of new well correlation concept in material balance model
Proposed reservoir correlation updated based on seismic data
New geological concept
New depositional model
Integration of core data
Changes in reservoir architecture
Main results and impact on field development
In need of an exploration boost, Norway doled out a record 83 production licenses in mature areas of the Norwegian Continental Shelf to 33 firms. This paper aims to develop new and improved empirical viscosity correlations through available field measurements on the Norwegian Continental Shelf (NCS). When a new horizontal well in Asia was incapable of unassisted flow, coiled tubing (CT) was selected for the perforation and stimulation intervention. Mechanical isolation was required to ensure that the stimulation fluids entered only the new zones. The company’s 31 licenses highlight a record-high total awarded during the latest APA round, which is aimed at developing mature areas of the NCS.
The private investment firm said it will partner with Treeline Well Services, one of the largest private rig providers in Canada, to build its service fleet following acquisition of the company. Treeline’s core areas are in Alberta and British Columbia. The decision to ramp down production on the Aspen project comes months after the Alberta provincial government imposed production cuts to handle pipeline bottlenecks. Aspen is projected to produce 75,000 BOPD upon startup. Canada will become the world’s fifth-largest producer of crude oil in 2017.
Portfolio analysis is based on the Nobel Prize-winning work of Harry Markowitz in the early 1950s in which he showed that the variance in results from a portfolio of stocks could be reduced by choosing stocks with a negative correlation. If two stocks are correlated negatively, when one stock is down the other stock will be up, and the portfolio will grow with very few wild swings.
Shale gas is becoming increasingly important globally. The nature of these reservoirs pose special considerations in reserves estimation. What follows was written in 2001 and needs to be updated based on current experience. Nonetheless, some of the considerations mentioned remain appropriate. As reported in mid-2000, natural gas produced from shale in the US has grown to be approximately 1.6% (0.3 Tcf annually) of total gas production.
Discovered resources of heavy and extraheavy crude oil are estimated to be approximately 4,600 billion bbl, two-thirds of which are in Canada and Venezuela. Bitumen and tar sands are excluded from this estimate. Published data on reserves estimates (RE) from this resource by primary drive mechanisms are sparse. Meyer and Mitchell estimated worldwide ultimate recovery from heavy and extraheavy crude oils to be 476 billion bbl, which is 10% of the Briggs et al. estimate of the discovered resource initially in place. Estimated primary reserves estimates (RE) ranges from 8 to 12% oil-in-place (OIP) for the Orinoco area of Venezuela, where stock-tank gravities range from 8 to 13 American Petroleum Institute (API).
The term "geopressure," introduced in the late 1950s by Charles Stuart of Shell Oil Co., refers to reservoir fluid pressure that significantly exceeds hydrostatic pressure (which is 0.4 to 0.5 psi/ft of depth), possibly approaching overburden pressure (approximately 1.0 psi/ft). Geopressured accumulations have been observed in many areas of the world. In regressive tertiary basins (the geologic setting for most geopressured accumulations), such pressures in sand/shale sequences generally are attributed to undercompaction of thick sequences of marine shales. Reservoirs in this depositional sequence tend to be geologically complex and exhibit producing mechanisms that are not well understood. Both of these factors cause considerable uncertainty in reserves estimates at all stages of development/production and reservoir maturity.