The compositional flow simulation model was frequently used to evaluate the miscible water alternating CO2 flooding (CO2-WAG). The uncertainty and sensitivity analysis have to be conducted to examine the parameters mostly affecting the performance of the process. Accordingly, multiple simulation runs require to be constructed which is a time-consuming procedure and finally increase the computational cost. This paper presents a simplistic approach to assess the miscible CO2-WAG flooding in an Iraqi oilfield through developing a statistical proxy model. The Central Composite Design (CCD) was employed to build the proxy model to determine the incremental oil recovery (ΔFOE) as a function of seven reservoir and operating parameters (permeability, porosity, ratio of vertical to horizontal permeability, cyclic length, bottom hole pressure, ratio of CO2 slug size to water slug size, and CO2 slug size). In total, 81 compositional simulation runs were conducted at field-scale to establish the proxy model. The validity of the model was investigated based on statistical tools; the Root Mean Squared Error (RMSE), R-squared statistic and the adjusted R-squared statistic of 0.0095, 0.9723 and 0.9507 confirmed the reliability of the model. The most influential and the optimum values of the parameters that lead to the higher ΔFOE during miscible CO2-WAG process were identified through proxy modeling analysis. The developed model was created based on the Nahr Umr reservoir in Subba oilfield and can be applied to roughly estimate the ΔFOE during the miscible CO2-WAG process at the same geological conditions as Nahr Umr reservoir.
Al-Shammari, Asrar (Kuwait Oil Company) | Gonzalez, Fabio A (BP Kuwait) | Gonzalez, Doris L (BP America) | Jassim, Sara (Kuwait Oil Company) | Sinha, Satyendra (Kuwait Oil Company) | Al-Nasheet, Anwar (Kuwait Oil Company) | Datta, Kalyanbrat (Kuwait Oil Company) | Younger, Robert (BP Kuwait) | Almahmeed, Fatma (Kuwait Oil Company)
Magwa-Marrat reservoir fluid is an asphaltenic hydrocarbon, exhibiting precipitation and deposition of asphaltene in the production system including the reservoir rock near wellbore and the tubing. The main objective of this work was to optimize production in Magwa-Marrat wells by remediation of tubing plugging and formation damage. Well interventions were prioritized based on potential production benefit resulting from the removal of productivity impairment. It was required to understand current formation damage in all wells, including those without recent pressure transient analysis (PTA).
All PTA tests since 1983 for Magwa-Marrat reservoir were analyzed to determine the different reservoir parameters such as flow capacity (KH), Skin (S), reservoir boundaries, and the extrapolated reservoir pressure (P*). PTA derived permeability was compared to log derived permeability to quality control skin determination. Independently formation damage was estimated using the radial form of the solution of the diffusivity equation for pseudo steady state flow. Once a skin correlation for both PTA vs. Darcy's law equation was derived using out of date well performance, the formation damage for all wells was accessed using current productivity index to identify production optimization opportunities in wells without recent PTA. This work was combined with nodal analysis to separate vertical lifting performance and inflow performance relationship impact on total productivity detriment.
Cross plot of PTA derived flow capacity (Kh) vs. Log derived Kh correlates very well with a slope and a coefficient of correlation close to 1.0. This was observed for wells located in the reservoir where there are not heterogeneities near wellbore such as boundaries or natural fractures. For these cases the higher than normally observed estimated skin explained poorer well productivity. After skin values were accessed for all wells, a production gain was estimated, and the wells were ranked based on potential benefit. A stimulation campaign was put in place based on the type of rock, formation damage and vertical lifting performance. Eight (8) wells were stimulated and they delivered approximately an additional 20% production for the field.
This work was innovative in the sense that there was not pressure build up tests run prior to the interventions and such, there was not any production deferral. This was achieved by building the well performance understanding on a correlation that required petrophysical description, production rates and estimates of drainage area reservoir pressure.
With maturing oil fields there is an increasing focus on improving the oil recovery factor and pushing the envelope toward a 70% target. This target is indeed very challenging and depends on a number of factors including enhanced oil recovery (EOR) methods, reservoir heterogeneities, displacement efficiency, and reservoir sweep. Other factors also play a role including vertical sweep due to flow behind the casing, well integrity issues, presence of conductive faults, or fractures. Proper surveillance performed to evaluate the injectant plume front, reservoir conformance, well connectivity, assessment of the integrity of wells, and other factors can be crucial for the success of the project and its future development.
The paper discusses special downhole logging techniques including a set of conventional multiphase sensors alongside high precision temperature (HPT) and high-definition spectral noise logging (SNL-HD). It was run to provide complete assessment of the injection – production distribution and any associated well integrity issues that might impair the lateral sweep of injectants into the target layer. This will be done for an injector and producer pair near the wellbore area. The operation was carried out with a tool string that contained no mechanical parts and was not affected by downhole fluid properties. It was conducted under flowing and shut-in conditions to identify flow zones and check fracture signatures. It also provided multiphase fluid velocity profiles.
The results of the survey allowed for in-depth assessment of borehole and behind casing flow, confirming lateral continuity, and provided an assessment of production-injection outside the pay zone. Results will allow for better well planning and anticipation of possible loss of well integrity that might impair production in the future. Combining the behind casing flow assessment with borehole multiphase flow distribution can be used for production optimization by sealing unwanted water contributing zones.
Mauddud Formation is a major oil-producing reservoir in Raudhatain Field of North Kuwait. The Mauddud Formation is an early Albian in age and it was generated an environment of the shallow-water carbonate and consists of Grainstones, Wackestones and Mudstones deposited in ramp settings. In Raudhatain field (RAMA) is undertaking massive development efforts with planned enhancement in Oil production. Reservoir description and distribution of rock properties in 3D space are challenging due to inherent reservoir heterogeneity, in this case primarily driven by depositional and diagenetic patterns.
KOC North Kuwait Reservoir Studies Team (NK RST) has been challenged to increase the production from several key NK oil fields. To achieve this goal, KOC has partnered with Schlumberger to rebuild integrated model with Petrophysics, Geophysics, and Geology and Reservoir data of the Mauddud Reservoir. The original model was required to minimize challenges in new infill locations, increase Oil recovery factor and detect water breakthrough to minimize water production. One of the key issues in creating RAMA reservoir model is integration of all available data in identifying the horizontal permeability, reservoir heterogeneity and identification of thief zones.
A fine Geological grid model with 35M cells, 10 Geological horizons has been built to characterize the Mauddud reservoirs of the RAMA field including the permeability from PLT logs combined with petrophysical and lithological / facies data to add more understanding of the distribution of reservoir properties. Log response group methodology and the undeveloped area in the Saddle (structurally low area) has been modelled for the first time in Raudhatain NK Field. This combined study utilizes the available data and cutting-edge technology using Geo2Flow which resulted in fluid compartmentalization and free water level identification. STOOIP has been upgraded and unlocking potential in new segments of the developed field. The original model was built based on vertical/Deviation wells (345) which lead to discrepancies in the structural interpretation. The new update has been carried out including all horizontal wells to minimize the uncertainty in the structure framework.
Al Jadi, Issa A. (Kuwait Oil Company) | Desai, Sameer Faisal (Kuwait Oil Company) | Al-Ghanim, Wafaa (Kuwait Oil Company) | Al-Wazzan, Roqaya M. (Kuwait Oil Company) | Al Sabea, Salem H. (Kuwait Oil Company) | Al Haddad, Saud M. (Kuwait Oil Company) | Franco, Francy Milena (Schlumberger) | Khor, Siew Hiang (Schlumberger) | Saxena, Aditya (Schlumberger) | Zhang, Qiong Michael (Schlumberger) | Hapsari, Hairuni Safri Tri (Schlumberger) | Elayaat, Ahmed A.Fouad. (Schlumberger) | Bodwadkar, Suhas V. (Schlumberger)
A proven and effective integrated asset modelling (IAM) approach has been adopted to bring multiple interdependent wells, pipelines networks, and process facilities models together into one single truly integrated asset model for the Greater Burgan Oilfield in Kuwait. The integrated wells-network facility models via the IAM platform also includes a water processing facility model which consists of 2 effluent water disposal plants; a crude oil export pipeline network and a water reinjection network model. This paper describes how a representative integrated asset model was developed for the Greater Burgan Oilfield through a model centric approach executed within an Integrated Operational Excellence (IOX) Program towards a Digital Transformation initiative by Kuwait Oil Company (KOC) South and East Kuwait (S&EK) Group together with Schlumberger. It also describes how this tool enables the asset teams to evaluate different operating scenarios to further enhance well performance and the overall asset productivity via rerouting well flow path to an appropriate header, identifying well workover opportunities, reevaluating artificial lift design, adding future wells (for field development) and comprehensive understanding of well integrity and flow assurance studies. The assessment was done not only at a gathering center (GC) level but also asset-wide level where the complete system constraints, interactions and back pressure effects between more than 2000 different wells were fully accounted. The simulated results such as pressure gradient, temperature gradient and erosional velocity ratio gradient across the production networks are presented on the GIS map for easy opportunity identification. The availability of this fully integrated asset model with up-to date calibrated wells and network models and process models enables KOC engineers to better understand current well performance and production potential, identify any possible bottlenecks imposed by the large complex surface network and process facilities of Greater Burgan Oilfield.
AL-Rashidi, Hamad (Kuwait Oil Company) | AL-Azmi, Waled (Kuwait Oil Company) | AL-Azmi, Talal (Kuwait Oil Company) | Ahmed, Ashfaq (Kuwait Oil Company) | Muhsain, Batoul (Kuwait Oil Company) | Mousa, Saad (Kuwait Oil Company) | AL-Kandari, Noor (Kuwait Oil Company) | AL-Sabah, Fahad (AL-Thurya) | AL-Hajri, Mohsen (BG) | AL-Mutwa, Bandar (AAA)
Crude oil production in Um-Ghdair field is consider one of the most complex operational activities in Kuwait Oil Company due to high water cut percentage, asphaletene flocculation, high viscosity and tight emulsion phenomena. As the fluid travels through the reservoir, wellbore, flowline, all the way to the gathering center, the state of initial equilibrium is disturbed leading to change in the chemical composition of the crude oil. As pressure and temperature continue to drop, and gas escapes, more asphaltenes and heavy components may continue to flocculate all the way throughout the system until the petroleum reaches its final destination. In this pilot project, asphaltene inhibitor and viscosity reducer agents were selected for reducing oil viscosity and breaking the tight emulsion phenomena in the selected piloting well in Um-Ghdair field. It was noticed that there is an asphaltene compounds flocculate in the interface between oil and water leading to increase crude oil viscosity. The best two among 22 chemical formulations tested through the screening process at lab scale and take it to pilot stage. Additionally, the pilot study examined the influences effective for surfactants such as water composition, temperature, concentration, pH and total dissolved solids. It was noticed that the viscosity reduction and the water separation improve with increasing surfactant concentration and increasing temperature up to 50 F. Two formulations were selected based on cost effective optimal concentrations of surfactant that identified from the bottle test. The pilot has been implemented successfully in the field, resulting a reduction in non-production time and increase the oil mobility from the reservoir.
Kharghoria, Arun (Kuwait Oil Company) | Alshammari, Abdulrahman (Kuwait Oil Company) | Gonzalez, Santiago (Kuwait Oil Company) | Sanwoolu, Ayodele Olusegun (Kuwait Oil Company) | Al-Rabah, Abdullah Abdul Karim (Kuwait Oil Company) | Montero, Jacobo (Shell) | Gonzalez, Gregorio (Shell)
This study presents the development of predictive models (for a single or a group of wells) to proactively manage water production in a heavy oil field in North Kuwait (average 16 °API crude, ~100 cp at 100 °F). Each resulting model is based on reservoir and time-dependent wellbore mechanical parameters that were obtained through several well tests. The primary goal is to be able to predict the water cut based on a well's current as well as "possible near-future" operating conditions.
The exercise involves using advanced predictive methodologies. Standard parameters (referred to as features) include water cut as the response variable along with pump intake pressure, total liquid production, pump speed, and surface unit power consumption among others as response variables. Random ForestsTM (RF), Alternating Conditional Expectations (ACE) and Multiple Linear Regression (MLR) algorithms were tested. Features were primarily grouped based on clustering. The predicted results are compared to history. The resulting models are further extended to generate type curves for future water cut prediction for different combinations of the operating parameters.
MLR and ACE based predictive models showed promising results. These algorithms also provided a way to characterize the influence of different features on the model outcome (
A predictive model is a time-saving tool compared to full range dynamic simulation. This provides a means to the engineers to establish an operating envelope for a well (or a group of wells) to manage water production. This is also a precursor to a full-scale machine learning process that could be implemented for automatically updating models when the quantity of data is expected to be massive in the coming years.
Qureshi, M Fahed (Texas A&M University at Qatar) | Ali, Moustafa (Texas A&M University) | Rahman, Mohammad Azizur (Texas A&M University at Qatar) | Hassan, Ibrahim (Texas A&M University at Qatar) | Rasul, Golam (Texas A&M University at Qatar) | Hassan, Rashid (Texas A&M University)
The hole cleaning is considered a key element of drilling operation as it impacts the economics of drilling operations, operational time of operations and the safety of operations. Inadequate hole cleaning can lead to blockages resulting in loss of circulation and premature wear out of the drill pipe. The transport of solids cuttings as a multiphase flow offers a solution to the hole cleaning issue, as it can aid to lower operational cost, reduce operation time, and enhance the quality of overall drilling operations.
Electrical resistance tomography (ERT) is a promising technology to visualize the 3D flow conditions involved in the hole cleaning process. ERT system is utilized to study and analyze the multiphase flow behavior and to provide in situ volume fraction distribution quantitatively through the drilling annulus. The motive of this work is to investigate the effect of different eccentricities (0-50 %), inner pipe rotation speed (0-120 RPM) and liquid flow rates (160-190 Kg/min) on the secondary phase (solids + air) transport across the annulus using the ERT system. The three-phase flow conditions (water, air, and solids) experiments were conducted in the horizontal flow loop with annulus at Texas A&M University at Qatar (TAMUQ) using ERT system. The flow loop annulus line consists of 6.16 m horizontal/inclined line. The inner diameter of the outer acrylic pipe and the outer diameter of the inner stainless steel pipe were 114.3 mm (4.5 in) and 63.5 mm (2.5 in), respectively. The glass beads (2-3 mm) were injected at a concentration of 5 wt%. The experimental results indicate that the ERT sensors have the capability of providing real-time quantitative images of annular multiphase flow regimes and it can be utilized effectively to observe the secondary phase (solids + air) transport across the opaque region of the annulus. It was also observed that the concentration of secondary phase (solids + air) tends to increase with an increase in the eccentricity of the inner pipe and the inner pipe rotation does not have a significant effect on the concentration of secondary phase (solids + air) at selected experimental conditions.
Desai, Sameer Faisal (Kuwait Oil Company) | Al Jadi, Issa (Kuwait Oil Company) | Al-Ghanim, Wafaa (Kuwait Oil Company) | Al Sabea, Salem H. (Kuwait Oil Company) | Al-Haddad, Saud M. (Kuwait Oil Company) | Franco, Francy Milena (Schlumberger) | Khor, Siew Hiang (Schlumberger) | Bodwadkar, Suhas V. (Schlumberger) | Saxena, Aditya (Schlumberger) | Zhang, Qiong Michael (Schlumberger) | Hapsari, Hairuni Safri Tri (Schlumberger) | Morales, Fernando (Schlumberger) | Halabe, Vijaya (Schlumberger)
Several efforts have been made in the past for generating an Integrated Asset Model (IAM) for the Greater Burgan field in Kuwait with mixed results on sustained utilization and benefits. A new effective full field Integrated Asset Model has now been developed within an Integrated Operational Excellence (IOX) program towards Digital Transformation of the Greater Burgan field. A proven model centric approach has been adopted to bring multiple interdependent wells, pipelines networks, and process facilities models together into one single truly integrated asset model. The IAM platform also includes a water processing facility model which consists of 2 effluent water disposal plants, a crude oil export pipeline network and a water injection network model. Development of this integrated wells-network-facility-crude export-water processing facility-water injection network model incorporating the 14 gathering centers in the South and East Kuwait (SEK) asset focused on providing all the essential valuable inputs to business processes for better asset management, faster and more accurate decision-making and optimizing the hydrocarbon flow path all the way from the reservoir till the export point. The assessment was done at full field level where the complete system constraints, interactions and back pressure effects between more than 2000 different wells were fully accounted up to the crude processing facilities. The availability of this fully integrated asset model with up-todate calibrated wells and network models and process models enables Kuwait Oil Company (KOC) engineers to better understand current well performance and production potential, identify any possible bottlenecks imposed by the large complex surface network and process facilities of Greater Burgan Oilfield. The simulated results such as pressure gradient, temperature gradient and erosional velocity ratio gradient across the production networks are presented on the GIS map for easy opportunity identification.
Deepwater reservoirs often consist of highly laminated sand-shale sequences, where the formation layers are too thin to be resolved by conventional logging tools. To better estimate net sand and hydrocarbon volume in place, one may need to leverage the high resolutions offered by borehole image logs. Traditionally, explicit sand counting in thin beds has been done by applying a user-specified cutoff on a 1D resistivity curve extracted from electrical borehole images. These workflows require multiple preprocessing steps and log calibration, and the results are often highly sensitive to the cutoff selection, especially in high-salinity environments.
This paper presents a new method that estimates sand fractions directly from electrical borehole images without extracting an image resistivity curve or applying any preselected cutoffs. The processing is based on an artificial neural network, which takes the 2D borehole image array as input, and predicts sand fractions with the measurements from all button electrodes. A cumulative sand count can be computed after processing the borehole image logs along an entire well by summing up the estimated net sands. The neural network is trained and tested on a large dataset from wells in a deepwater reservoir with various degrees of laminations, and validated with sand fractions identified from core photos. Upon testing, a good match has been observed between the prediction and the target output. The results were also compared against another sand-counting method based on texture analysis, and showed advantages of yielding unbiased estimations and a lower margin of error.