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The harvest of hydrocarbon from the depleted reservoir is crucial during field development. Therefore, drilling operations in the depleted reservoir faced several problems like partial and total lost circulation. Continuing production without an active water drive or water injection to support reservoir pressure will decrease the pore and fracture pressure. Moreover, this depletion will affect the distribution of stress and change the mud weight window. This study focused on vertical stress, maximum and minimum horizontal stress redistributions in the depleted reservoirs due to decreases in pore pressure and, consequently, the effect on the mud weight window. 1D and 4D robust geomechanical models are built based on all available data in a mature oil field. The 1D model was used to estimate all mechanical rock properties, stress, and pore pressure. The minimum and maximum horizontal stress were determined using the poroelastic horizontal strain model. Furthermore, the mechanical properties were calibrated using drained triaxial and uniaxial compression tests. The pore pressure was tested using modular dynamic tester log MDT. The Mohr–Coulomb model was applied in the 4D model to calculate the stress distribution in the depleted reservoir. According to study wells, the target area has been classified into four main groups in Mishrif reservoir based on depletion: highly, moderately, slightly, and no depleted region. Also, the results showed that the units had been classified into three main categories based on depletion state (from above to low depleted): L1.1, L1.2, and M1. The mean average reduction in minimum horizontal stress magnitude was 322 psi for L1.1, 183.86 psi for L1.2, and 115.56 psi for M1. Thus, the lower limit of fracture pressure dropped to a high value in L1.1, which is considered a weak point. As a result of changing horizontal stress, the mud weight window became narrow.
The harvest of hydrocarbon from the depleted reservoir is crucial during field development. Therefore, drilling operations in the depleted reservoir faced several problems like partial and total lost circulation. Continuing production without an active water drive or water injection to support reservoir pressure will decrease the pore and fracture pressure. Moreover, this depletion will affect the distribution of stress and change the mud weight window. This study focused on vertical stress, maximum and minimum horizontal stress redistributions in the depleted reservoirs due to decreases in pore pressure and, consequently, the effect on the mud weight window. 1D and 4D robust geomechanical models are built based on all available data in a mature oil field. The 1D model was used to estimate all mechanical rock properties, stress, and pore pressure. The minimum and maximum horizontal stress were determined using the poroelastic horizontal strain model. Furthermore, the mechanical properties were calibrated using drained triaxial and uniaxial compression tests. The pore pressure was tested using modular dynamic tester log MDT. The Mohr–Coulomb model was applied in the 4D model to calculate the stress distribution in the depleted reservoir. According to study wells, the target area has been classified into four main groups in Mishrif reservoir based on depletion: highly, moderately, slightly, and no depleted region. Also, the results showed that the units had been classified into three main categories based on depletion state (from above to low depleted): L1.1, L1.2, and M1. The mean average reduction in minimum horizontal stress magnitude was 322 psi for L1.1, 183.86 psi for L1.2, and 115.56 psi for M1. Thus, the lower limit of fracture pressure dropped to a high value in L1.1, which is considered a weak point. As a result of changing horizontal stress, the mud weight window became narrow.
Brownfield field development plans (FDP) must be revisited on a regular basis to ensure the generation of production enhancement opportunities and to unlock challenging untapped reserves. However, for decades, the conventional workflows have remained largely unchanged, inefficient, and time-consuming. The aim of this paper is to demonstrate that combination of the cutting-edge cloud computing technology along with artificial intelligence (AI) and machine learning (ML) solutions enable an optimization plan to be delivered in weeks rather than months with higher confidence. During this FDP optimization process, every stage necessitates the use of smart components (AI & ML techniques) starting from reservoir/production data analytics to history match and forecast. A combined cloud computing and AI solutions are introduced. First, several static and dynamic uncertainty parameters are identified, which are inherited from static modelling and the history match. Second, the elastic cloud computing technology is harnessed to perform hundreds to thousands of history match scenarios with the uncertainty parameters in a much shorter period. Then AI techniques are applied to extract the dominant key features and determine the most likely values. During the FDP optimization process, the data liberation paved the way for intelligent well placement which identifies the "sweet spots" using a probabilistic approach, facilitating the identification and quantification of by-passed oil. The use of AI-assisted analytics revealed how the gas-oil ratio behavior of various wells drilled at various locations in the field changed over time. It also explained why this behavior was observed in one region of the reservoir when another nearby reservoir was not suffering from the same phenomenon. The cloud computing technology allowed to screen hundreds of uncertainty cases using high-resolution reservoir simulator within an hour. The results of the screening runs were fed into an AI optimizer, which produced the best possible combination of uncertainty parameters, resulting in an ensemble of history-matched cases with the lowest mismatch objective functions. We used an intuitive history matching analysis solution that can visualize mismatch quality of all wells of various parameters in an automated manner to determine the history matching quality of an ensemble of cases. Finally, the cloud ecosystem's data liberation capability enabled the implementation of an intelligent algorithm for the identification of new infill wells. The approach serves as a benchmark for optimizing FDP of any reservoir by orders of magnitude faster compared to conventional workflows. The methodology is unique in that it uses cloud computing technology and cutting-edge AI methods to create an integrated intelligent framework for FDP that generates rapid insights and reliable results, accelerates decision making, and speeds up the entire process by orders of magnitude.
The objective of this paper is to share Dragon Oil's experience to overcome challenges in drilling deep depleted reservoirs in the Cheleken Contract Area, Caspian Sea, Turkmenistan. Dragon Oil operates two producing oilfields; Lam and Zhdanov. The main targets described in this paper are from the Zhadanov field with the deepest target reaching around 4000m. This paper describes the challenges and experience from drilling complex wells in this area. The wells described in this paper penetrate various depleted reservoirs due to past production. The deepest reservoir consists of a sequence of interbedded sands with varying pore pressure due to geology and depletion due to production. The intra-reservoir shale layers are unstable and require high Mud Weights, often reaching technical limits (up to 20 ppg). The requirement of high mud weight to address shale instability is in contrast with the solutions for drilling depleted sand and the common drilling risks are mud losses and stuck pipe. An analysis of challenges, risk mitigations and lessons learned are discussed in this paper. Drilling a deep 6" directional hole is challenging and it requires continuous monitoring of the drilling fluid density. The pre-drill well planning is a key solution for preparing to deal with operational challenges and develop a strategy for reducing risk. This paper describes the important steps followed in the well planning process, risk mitigation options analyzed and incorporating the key elements in drilling program to overcome challenges. Some of the lessons learned from the experience of delivering wells in such complex area are: Implement 3D/4D PPFG and wellbore stability models to communicate depletion magnitudes accurately and effectively incorporate the risk in well planning to enable drilling with minimum overbalance.Map the correlation between depleted sand bodies (e.g. facies model) and assess impact of fault typesImplement Synthetic Oil Based Mud to improve wellbore condition and enhance drilling performance.Choose well trajectories to help transfer weight/torque to bottomMinimize the BHA static time across severely depleted zones This paper shares the experience from Turkmenistan and lessons learned from Dragon Oil's operations in Caspian Sea. This paper consolidates the well planning process, methods adopted to incorporate risk mitigation into drilling program and our recent experience from drilling complex wells targeting deep depleted reservoirs.
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